Test your AI before launch. Stop live attacks before they become business risk.

You made the AI decision.
Now secure it.

With SAMI get true defense in depth for AI.
Validate AI vulnerabilities across LLMs, RAG, MCP, agents, external-facing AI, Shadow AI, and AI supply-chain exposure before and continuously after deployment. Protect live AI workflows across prompts, responses, RAG, agents, MCP/tool calls, sensitive data, get AI workflow evidence and compliance for AI systems.

The result: AI teams can move faster, security teams stay in control, and leaders get proof that AI systems is governed for security, privacy and compliance.

See your AI security gaps →
Aligned with
OWASP LLM Top 10MITRE ATLASEU AI ActNIST AI RMFISO 42001ISO 27001SOC 2 Type IIZero Trust
99%
Prompt-attack detection efficacy5
~30ms
Low-latency detection target5
300+
AI attack techniques tracked to protect live AI workflows5
300+
Privacy elements monitored to protect data used by AI systems5
65+
Compliance controls mapped to help AI stay aligned with required frameworks5
70%
Live MCPs showed exposed risk paths6

Pre-Built AI Defense That Scales

Deploy AI faster with SAMI’s pre-built defense-in-depth platform, ensuring AI operate securely within compliance requirements while keeping your data private, unlike static DIY engineered solutions that do not scale.

Brands We Work With

Control Your AI
Before It Controls Your Risk

AI is no longer just a chatbot. It now connects to apps, data, agents, tools, and real business actions. That means every AI workflow can create risk: vulnerabilities in your LLM, RAG, MCP etc., hidden Shadow AI, external facing AI, data leaks, prompt attacks, RAG poising, unsafe agents, compliance gaps etc.

SAMI sees, records, secures the full AI path while keeping it functioning within your compliance framework, so your organization can innovate safely.

SAMI gives you one platform to address all of this

Discover, secure, govern, and continuously monitor AI before, during, and after production. SAMI helps you see every AI system, find exposed risk, stop live attacks, protect sensitive data, manage AI vulnerabilities by business impact, enforce policies, and prove what happened. From first deployment to live production, SAMI keeps AI visible, protected, and accountable.

See every AI system
Inventory LLMs, prompts, agents, MCP tools, RAG pipelines, AI applications, external-facing AI, AI supply-chain exposure, and Shadow AI.
Find exposed AI risk
Identify externally reachable AI systems, exposed AI endpoints, risky agent workflows, vulnerable MCP connections, unmanaged AI use, and AI assets operating outside approved controls.
Stop live AI attacks
Block risky prompts, unsafe responses, malicious agent actions, MCP/tool abuse, session erosion, jailbreak attempts, and dangerous AI workflows before they create business impact. Protect private data
Monitor AI drift and control gaps
Detect when models, prompts, guardrails, RAG sources, agents, tools, policies, or workflows change in ways that create new risk.
Sensitive Data Protection
Detect, redact, tokenize, or block credentials, regulated data, personally identifiable information, proprietary data, and business-sensitive datasets before they are exposed, uploaded, shared, or processed unsafely.
Keep AI within policy and compliance
Continuously assess whether AI activity aligns with internal policies, privacy requirements, compliance frameworks, approved workflows, and governance rules.
Prevent poisoned AI decisions
Screen RAG content so poisoned, stale, untrusted, or sensitive sources do not shape AI answers or downstream actions.
Prove what happened
Capture runtime evidence for every prompt, model call, retrieved source, agent action, MCP/tool call, data access, policy decision, privacy assessment, control action, and enforcement result.
Find and validate AI vulnerabilities
Validate weaknesses across LLMs, RAG, MCP, agents, AI applications, external-facing AI, Shadow AI, AI supply chain, privacy controls, and guardrail coverage.
The gap is real

Most organizations are deploying AI
faster than they can secure it.

AI moves at product speed. Security moves at audit speed. The gap between them is where breaches, fines, and board questions come from.

SAMI closes it, before launch and continuously after, so AI teams can move faster and security teams stay in control.

SAMI sees, records, and secures the full AI path, keeping it in compliance and your data private before risk becomes liability.
93.2%
of organizations lack full confidence in securing AI-driven data
BigID, 2026
80.2%
are unprepared for AI regulatory compliance
BigID, 2026
69.5%
cite AI-powered data leaks as their top security concern
BigID, 2026
92%
of GenAI users with AI guidelines still leaked company data
Oliver Wyman, 2026
Measured outcomes from live deployments

Customer Stories

"SAMI changed how our board understands cyber risk from technical noise to financial clarity. For the first time, they're asking the right questions."
C
CISO
Critical Infrastructure Organization
"We reduced remediation timelines by 70% in the first quarter. SAMI didn't just find the risk, it told us what to fix first and proved it worked."
V
VP Security
Global Enterprise Organization
Global Standard · OWASP LLM Top 10 v1.1

These are the 10 attack vectors
the global security community has documented.
SAMI addresses all 10 configured for you, active from day one.

OWASP is the global authority that defined web application security for 25 years. Their LLM Top 10 is the definitive framework for AI attacks, peer-reviewed, internationally recognized, used by regulators worldwide. Every one of these attack vectors exists in your AI environment today. SAMI is pre-built to cover all 10. No development. No security engineering. No configuration required.

All 10 attack vectors covered
Every OWASP LLM Top 10 vector is addressed by at least one SAMI layer. Most are covered by two or three simultaneously.
Preset · No development required
These are not rules you write. They are protections SAMI deploys automatically. No security engineer. No YAML configuration. No ongoing maintenance.
Continuously updated as threats evolve
OWASP updates the framework. New techniques emerge daily. SAMI's Autonomous Response Engine updates all layers automatically when new attack patterns are detected.
Click to see Top 10 AI risks and How SAMI handles it
80% of companies would fail an AI governance audit today

Act before an incident.
The documentation exists either way.

🇪🇺
EU AI Act
Regulation (EU) 2024/1689
ACTIVE
2 August 2026:
until Article 9 full enforcement

Articles 9, 13, 17 require documented continuous risk management. Penalty exposure can reach €35M or 7% for prohibited AI practices, and €15M or 3% for many operator obligations. SAMI generates proof automatically.

"Can you produce Article 9 evidence today?"
🇺🇸
FTC Section 5
Federal Trade Commission Act
ENFORCING
Now
: no new law required

AI companies face FTC action for biased outputs, data misuse, and hallucinations. "The model did it" is not a defense.

"Are your AI outputs auditable?"
HIPAA + AI
OCR Guidance
ENFORCING
$1.9M
per violation per year

For covered entities and business associates, AI systems that contain or use ePHI must be governed under HIPAA technical safeguards. SAMI tokenizes PHI before model contact, PHI never reaches the model.

"Can you prove PHI never reached your model?"
SEC Cybersecurity Rule
17 CFR §229.106
ACTIVE
4 days
to disclose material incidents

Material AI cyber incidents must be disclosed within 4 business days. SAMI generates 10-K-ready AI risk documentation automatically.

"Is your AI risk documented for your 10-K?"
🇺🇸
Colorado AI Act SB 205
In force February 2026
ACTIVE
Now
: template for 8+ states

Risk management documentation, bias audits, consumer disclosures required. SAMI maps to all SB 205 obligations automatically.

"Is your AI risk management documented?"
AI Litigation Standard
Courts · Establishing now
ACTIVE
Now
: Air Canada ruling cited in 3 cases

Courts are establishing the standard of care for AI. Organizations with documented continuous monitoring survive it. Those without will not.

"Do you have a continuous AI audit trail?"
The only question that matters

Before or after
an incident?

SAMI gives you the choice. Run your AI Security Assessment before anyone else finds what's there.
See which of these are live
in your environment right now.

Free External Scan

1 https://home.bigid.com/download-ai-risk-report?utm_source=chatgpt.com

2 https://www.oliverwymanforum.com/uncharted/employees-are-leaking-company-data-via-public-gen-ai-tools.html

3 https://www.ibm.com/reports/data-breach

4 https://www.gartner.com/en/newsroom/press-releases/2026-05-26-gartner-says-applying-uniform-governance-across-ai-agents-will-lead-to-enterprise-ai-agent-failure

5 Performance metrics are based on results from internal and external benchmark testing.

6 Based on SAMI Assessments on MCP Marketplace.

Trusted by leading organizations
Home › Workflow Intelligence
Workflow Intelligence

Every agent. Every database. Every row.
Every AI request, fully visible.

SAMI Workflow Intelligence maps the complete execution path of every AI interaction in real time, not just what went in and came out, but everything that happened in between.

Full Call Tree

The complete execution path for every request: entry → AI Firewall → Guardrails → Orchestrators → Sub-orchestrators → Parallel agent swarms → RAG pipelines → LLM refinement passes → Output Firewall → Response. Every node, every branch, every decision point.

When a breach investigation asks "what did our AI do?", this is the answer. Not a summary. The full trace.
Data Provenance per Prompt

Every database accessed, name, rows read, sensitivity classification (LOW/MEDIUM/HIGH), and whether it was within the declared session policy. Every RAG index searched, every chunk retrieved, similarity scores, what made it into model context. Every file read.

Your model said X. It said it because it retrieved Y from contracts_db (503 rows, HIGH sensitivity, out of scope). That's the data lineage EU AI Act requires.
Violation Propagation Tracing

Violations detected at the point of occurrence and tracked through every downstream step. When agent A07 accesses an out-of-scope database, that flag propagates through the merger, through two LLM refinement passes, and is caught again at the output firewall with the full propagation chain documented.

Without propagation tracing, you know something was flagged. With it, you know exactly how far it travelled before it was stopped and what it touched.
What SAMI Workflow Intelligence documents for every request
The system provides end-to-end AI workflow observability and governance by tracking source identity, AI firewall decisions, guardrail validations, orchestration plans, agent executions, database access, RAG pipeline activity, MCP/tool calls, LLM invocations, output firewall enforcement, workflow vitals, and exportable audit records.
For your CISO

"Show me every AI interaction that accessed sensitive data outside declared scope in the last 30 days." SAMI answers that in seconds, not weeks of log forensics.

For your regulator

EU AI Act Article 9 requires documented continuous risk management. Workflow Intelligence generates that documentation automatically, not a periodic report, a live audit trail for every single request.

For your legal team

"Our AI accessed contract data it shouldn't have. Here is the exact request ID, the exact agent, the exact rows, the exact timestamp, and the violation flag that was raised and escalated." That's a defense. "We don't know" is not.

 Workflow Intelligence: Complete AI Interaction Audit Trail

SAMI sees every step
of every AI interaction.

For every single AI request, SAMI's Workflow Intelligence maps the entire execution path from entry through firewall, guardrails, orchestrators, parallel agent swarms, RAG pipelines, and LLM refinement passes, to the output firewall and final response. Every agent, every database, every row count, every MCP tool call, every violation, visible, traceable, and documented.

When your regulator asks what your AI system did and under EU AI Act Article 9, they will, this is the record that answers it. When a data breach investigation asks "how did agent A07 pull 503 rows of contract data that was never in the declared session policy?", this is what shows you exactly how it happened, where it propagated, and what it touched on the way out.

if RAG invoked if agent invoked
SOURCE Prompt in
L01 AI Firewall 4-direction inspect
L02 Guardrails Policy-as-runtime
L05 Tokenization PII stripped
LLM Language Model Processes tokens only
L03 RAG Defender Query + content check
L04 MCP Security Pre-execution gate
OUTPUT Response out
SAMI AUDIT Full trail EU AI Act

↑ Click any checkpoint to see what SAMI captures in the audit trail

Most organizations can't answer the question: "What did your AI model do at 09:14 on Tuesday?" SAMI can. Every checkpoint in this flow produces a tamper-evident audit entry, who, what, which data, which decision, how long. That record is your EU AI Act compliance evidence, your insurer's proof of monitoring, and your legal defense if something goes wrong.

Prompt Source

Who sent this? From where? SAMI knows before the model does.

Every interaction starts with identity capture. Without it, you can't answer the first question a regulator or insurer asks: who initiated this? SAMI records the full source context before the prompt touches anything, so you always know.

Audit trail: source capture
User identityemployee@company.com · Entra ID verified
Source IP192.168.1.44 · Internal VPN
Timestamp2025-03-14 09:14:22 UTC
ApplicationCustomer support portal · v2.4.1
Session IDsess_a3f9b2c1 · 4 prior turns
Full identity contextSession historyEU AI Act evidence
Layer 01: AI Firewall

Reads intent, not just syntax.
4 directions. 5 hard decisions.

The AI Firewall inspects the prompt before the model sees it, but also inspects what the model returns, what the RAG pipeline retrieves, and what any agent tool call attempts. All 4 directions, simultaneously. Five hard outcomes: ALLOW · QUARANTINE · REDACT · TOKENIZE · BLOCK. No probability scores. No ambiguity.

When the Firewall fires QUARANTINE, three things happen at once: the content is held, a SOC incident is created in your SIEM, and the interaction is flagged in the audit trail. Your security team sees it. The regulator's evidence trail is updated. And the model never touched the threat.

Audit trail: AI Firewall decision
DirectionInbound prompt
Semantic scanPrompt injection intent detected, instruction override pattern
DecisionQUARANTINE held for SOC review
SOC incidentAI-INC-2025-0314-001 raised in SIEM
Response time38ms
★ Only product inspecting all 4 directions ★ QUARANTINE workflow, no competitor OWASP LLM01OWASP LLM02
Layer 02: Dynamic Guardrails (TrustGuard)

Policy-as-runtime. Session memory.
Auto-tightens when bypass is detected.

Guardrails check the prompt against your current policy configuration, hard allow/block/transform decisions, not probability scores. Session memory reviews the last 20+ turns for multi-step erosion patterns.

Here's what matters: a sophisticated attacker doesn't try one obvious injection. They probe across 10 or 20 messages, slowly shifting the conversation. By message 15, your static guardrail has no idea it's being manipulated. SAMI's session memory catches the pattern. When bypass is detected, guardrails auto-tighten and a SOC alert fires simultaneously, while the attack is still in progress, not after it succeeded.

Audit trail Guardrail decision
Policy versioncustomer-support-v3.2 · updated 2025-03-01
Session turns4 prior turns reviewed
Erosion patternNone detected single-turn prompt
Policy matchALLOW prompt within customer support scope
Guardrail statusStable, no auto-tighten triggered
★ Auto-tighten + SOC alert simultaneously ★ Session memory across messages Policy-as-runtime
Layer 03: Data Privacy & Tokenization

PII stripped before model contact.
The model never sees the sensitive data.

Before the prompt reaches the LLM, SAMI tokenizes PII, PHI, and proprietary data locally. The model processes tokens only it never sees a customer name, account number, or medical record. SAMI rehydrates real values in the output on the way back.

Why this matters: Samsung's 2023 ChatGPT leak engineers pasted source code and internal data into a public LLM happened because nothing stopped the data at the point of model contact. SAMI's tokenization means the model never sees the sensitive data in the first place. Supports GDPR Article 25 privacy-by-design by tokenizing sensitive data before model contact.

Audit trail: Tokenization
PII detectedCustomer name · Account number · Email address
ActionTokenized locally, TKN_7f2a, TKN_8c1d, TKN_3e9b
Sent to LLMTokens only, no PII in model context
RehydrationApplied on output, user sees real values
TelemetryZero-audit log local only
★ Full capability with zero vendor telemetry GDPR Art.25SaaS + On-Prem
LLM: Language Model

The model sees only
what SAMI allows it to see.

By the time the prompt reaches the LLM, it has passed the AI Firewall, cleared guardrail policy, and had PII stripped and tokenized. The model processes the sanitized, tokenized request. SAMI continues to inspect the outbound response gets the same treatment on the way back out through L01 and L05.

Audit trail: LLM invocation
ModelGPT-4o · customer-support deployment
Input tokens342 (sanitized) · 0 PII in context
Prompt hashsha256:a3f9b2c1... (tamper-evident)
ResponseGenerated · passing to L01 outbound check
Layer 04: RAG Defender

Which folder. Which documents. What was retrieved.
Complete RAG provenance.

When the LLM invokes a RAG call, SAMI intercepts at both the query (before it hits the knowledge base) and the retrieval (before returned content reaches the model). Logs exactly which knowledge base, which folders, which documents, what was retrieved.

This matters for two reasons. First, RAG poisoning where an attacker plants malicious instructions inside documents your AI retrieves is stopped here before model contact. Second, you now have a complete data lineage record for every AI response: your model said X because it retrieved Y from folder Z. That's the provenance your legal team needs when an AI-generated response causes a problem.

Audit trail: RAG retrieval
KB queriedcustomer-support-kb · v2024-Q4
Folders accessedpolicies/ · products/pricing/
Docs retrieved3 documents · policy-v4.pdf, pricing-Q4.pdf, faq-returns.md
Poisoning scanClean, no injection patterns detected
DecisionALLOW content passed to model context
★ Only dual-layer RAG protection available ★ Full folder/document provenance OWASP LLM03
Layer 05: Agentic & MCP Security

Every tool call validated
before it executes.

When the LLM invokes an agent or MCP tool call, SAMI validates it against your approved policy scope before the action executes. The database is never queried. The email is never sent. The file is never written, unless policy says so.

Without this, your AI agent is executing actions on your behalf with no governance layer. It's the equivalent of giving a contractor building access with no supervision and no keycard log. Every tool invocation, every scope check, logged, so you know exactly what your agents did and when.

Audit trail: MCP tool call
Tool invokedcustomer-db.lookup(account_id)
Policy scopeRead · customer-support · approved
DecisionALLOW within approved scope
Data returnedAccount status · support tier · open tickets
Write attemptNone detected
★ Only pre-execution MCP policy gate OWASP LLM08Dual approval
Audit Trail Complete

Every step. Every decision.
Always ready for inspection.

SAMI generates a tamper-evident audit record for every AI interaction, from source identity to final response.

EU AI Act Article 9 requires documented lifecycle risk management for high-risk AI systems, not a one-time assessment, continuous proof. Cyber insurers are adding AI monitoring clauses to renewals. Courts are establishing the standard of care for AI negligence. This audit trail is what separates "we had controls in place" from "we had no idea what our AI was doing." One is a defense. The other is liability.

Complete interaction record
Interaction IDAI-INT-2025-031400142
Total latency312ms including all SAMI layers
Layers checkedL01 · L02 · L03 · L04 · L05: all PASS
EU AI ActArt.9 evidence record updated
ExportableJSON · PDF · SIEM event
EU AI Act Art.9 Insurance evidence Forensic record

Your AI environment is live.
SAMI is watching it.

Free external scan. Or book a proof of value, we deploy in your environment, show you everything we find, and produce the board and compliance report before we leave.

See what SAMI finds in your AI environment.
Free assessment. No code changes.
Home › SAMI for AI
SAMI: AI Security Platform

You deployed AI.
Now it needs to be
secured end to end.

AI is no longer one model behind one app. It is a live workflow across prompts, responses, RAG pipelines, agents, MCP tool calls, sensitive data, and downstream actions. Every part of that workflow is an attack surface.

SAMI is the only platform built to protect all of it before launch, in production, and continuously as AI changes. One platform. Defense in depth. No gaps.

OWASP LLM Top 10 MITRE ATLAS EU AI Act NIST AI RMF ISO 42001 Zero Trust
The state of AI security right now
92%
of organizations with AI guardrails have already leaked data. World Economic Forum, 2024
40%
of enterprises will demote or decommission AI agents because governance gaps are found after production incidents. Gartner, 2026
Article 9
EU AI Act requires continuous lifecycle risk evidence for high-risk AI systems. Enforcement is active. Penalties are material.
70%
of live MCP deployments showed exposed risk paths in SAMI testing.

Every feature. Every competitor.
One table.

Click to view How SAMI AI Security compares
Why this is hard

AI deployment creates new compounding
security problems.

AI is not just another application. It is a new operating layer where prompts, data, models, agents, tools, and business decisions all interact in real time.

Traditional security tools were built to detect malicious code, malware, network anomalies, and suspicious API behavior. AI risk does not always look like that. A prompt injection looks like a normal request. A poisoned RAG document looks like trusted content. An agent hijack looks like an approved workflow. A data leak looks like a helpful answer.

That is why AI security requires more than point controls. Organizations need visibility, runtime protection, privacy enforcement, policy control, vulnerability management, and full observability across every AI system.

Click to see issues with AI Deployment
The platform at a glance

10 layers of protection.
Every one solves something the others cannot.

Each layer below links to its own detail page. This is the summary one line per layer, what it does, why it exists.

Click to see 10 Layers of protection
What each team gets

One platform.
Answers for every team that touches AI.

Security & SOC
Turn AI attacks into incidents your SOC can act on.
✓300+ AI attack vectors covered out of the box, with ability for further customization
✓Structured AI incidents in your existing SIEM queue
✓Full call-tree evidence for every incident, including privacy assessment for data accessed
✓Policy tightens automatically when bypass is detected
✓Allow, block, redact, tokenize, alert, or escalate actions
✓No code changes to your existing AI stack
Risk & Compliance
Keep AI operating inside your policies, regulations, and control framework.
✓Map AI activity to corporate policies, privacy rules, and compliance frameworks
✓Enforce approved data privacy rules at runtime
✓Use preset policy packs or customize rules to match your organization
✓Track every prompt, response, tool call, policy decision, and exception
✓Generate audit-ready evidence showing what happened, what was allowed, what was blocked, and why
AI & Engineering
Ship AI faster without creating security debt.
✓Deploy controls without slowing AI teams
✓Test prompts, responses, agents, RAG, MCP, and tools before production
✓Enforce approved policies from development into runtime
✓Detect risky behavior before it becomes an incident
✓Keep controls aligned as models, data, tools, and workflows change
AI Vulnerability Management
Prioritize AI risk by business impact and fix what matters first.
✓Check AI systems exposed to the internet, LLM, RAG, MCP etc. for vulnerabilities continously both before and after deployment.
✓Business impact assessment for every AI finding
✓Remediation modeling to show which fixes reduce the most risk
✓Remediation orchestration across owners, teams, and workflows
✓Continuous gap assessment against policies and compliance frameworks
✓Evidence-ready reporting for audits, reviews, and leadership

From AI guardrails to
AI control evidence

Guardrails can help decide whether an interaction should continue. But enterprise AI security needs more: visibility into every workflow, control over every agent action, validation of every exposure, business impact for every risk, and evidence for every remediation.

SAMI combines runtime AI security with CTEM for AI so security, AI, risk, and compliance teams can protect AI in production and prove it.

Click to see Full capability comparison
Deployment modes

SaaS or on-premises.
Same features. Your choice.

Most AI security tools require cloud connectivity. SAMI works fully on-premises with zero telemetry, full GDPR compliance by architecture, and air-gap available from day one.

SaaS
Cloud-hosted · Managed · Deploy in hours

SAMI hosted and managed by Autnhive. Automatic updates, zero infrastructure overhead. Connect your AI environment in hours.

On-Premises
Your infrastructure · Full data sovereignty · GDPR by architecture

SAMI runs entirely in your environment. No data leaves your perimeter. Zero vendor telemetry. Air-gap available. GDPR Article 25 privacy-by-design. Full feature parity with SaaS.

The only question that matters

The question is not
whether to secure your AI.
It is whether you discover
the need before or after an incident.

Every organization in the incident list above had time to act before it happened. The vulnerability existed. The attack surface was there. The question was simply whether anyone looked. SAMI's free AI Security Assessment looks. It shows you exactly what is exploitable in your AI environment right now the same things an attacker would find.

See what SAMI finds in your AI environment.
Free assessment. No code changes.
Home › Firewall & Trusguard

Your AI is running on guardrails
nobody has updated in months.
SAMI fixes that automatically.

Keeping guardrails current requires constant manual effort from security, privacy, and compliance teams. As AI attacks evolve, privacy rules change, compliance frameworks update, and corporate policies shift, yesterday's guardrails become today's gaps.

Prompt injection Session erosion Sensitive data exposure RAG poisoning Agent & MCP tool-call risk Compliance violations SOC blind spots Shadow AI Guardrail gaps Model drift

Guardrails were written once.
Your AI risk changes every day.

AI doesn't fail in one place. A prompt can ask for something unsafe. A response can expose sensitive data. A RAG document can inject hidden instructions. A conversation can slowly walk the AI outside policy, one message at a time. An agent can turn a bad instruction into a real-world action. And to your SIEM, every one of those events looks like normal API traffic.

Three layers of rules.
All enforced at runtime.

SAMI sits inside your AI workflow and evaluates every interaction before risk reaches your model, users, tools, or downstream systems.

✓
Thousands of preset checks out of the box.STARTS DAY ONE
OWASP LLM Top 10, MITRE ATLAS, prompt injection, jailbreak patterns, PII/PHI/credentials, RAG poisoning, MCP tool-call risk, session erosion, EU AI Act, HIPAA, NIST, ISO 42001. You don't start from zero. You start from comprehensive.
✓
Your own rules on top.CUSTOMIZABLE
Every organization draws different lines. Add your own policies, approved workflows, privacy rules, data handling logic, department-specific limits, and human review triggers. Written policy becomes runtime enforcement, no developer required.
✓
Gap assessment that never stops.★ UNIQUE
New attack technique published? Framework updated? Model version changed? Shadow AI discovered? SAMI finds the gap, shows you the exposure, and closes it automatically once you approve, no developer work, no downtime.
SAMI does not replace what you have. It ingests your existing guardrails, fills the gaps, and adds the runtime enforcement layer around everything, no code changes to your AI stack.
Preset coverage includes
OWASP LLM Top 10 MITRE ATLAS EU AI Act HIPAA NIST AI RMF ISO 42001 GDPR Art.25 PII · PHI · Secrets Prompt Injection Jailbreaks RAG Poisoning MCP Tool-Call Risk Session Erosion Shadow AI Model Drift Compliance Gaps + Your own rules, policies & policy packs
The question every CISO eventually faces
"When your AI violated policy last week, did your security team see it in real time? Or did you find out from a user complaint?"

Not just keywords.
Intent, context, session behavior, and the action the AI is trying to take.

SAMI inspects all four directions simultaneously inbound prompts, outbound responses, RAG retrieved content, and every MCP/agent tool call before anything reaches your model.

Prompts & Responses
4-direction coverage
Inbound prompts. Outbound responses. RAG retrieved content. MCP/agent tool calls. All four, simultaneously, before the model sees them.
Session Behavior
Multi-turn erosion detection
20+ turns held in session memory. A conversation that slowly walks the AI outside policy, caught before it crosses the line, not after.
Sensitive Data
PII · PHI · Secrets · Proprietary
Detected in prompts, responses, and retrieved content, before the model ever processes it.
RAG Pipeline
Two checkpoints, not one
Documents screened at ingestion before entering the knowledge base, and again at query time before retrieval reaches the model.
Agents & MCP
Validated before execution
Every agent tool call checked against approved policy scope before it runs, not logged after the damage is done.
Guardrail Gaps
Continuous coverage assessment
Controls that should exist but don't identified continuously against live attack patterns, framework updates, and your evolving AI stack.
Five enforcement decisions

When SAMI finds something,
your team decides what happens next.

Not every risk is the same. SAMI gives you hard enforcement outcomes not probability scores, not flags. Decisions. Auditable. Traceable. Enforceable by design.

Every runtime decision is logged with request ID, component-level decisions, reason metadata, and tenant context. Mean-time-to-investigate: minutes, not hours.

The CISO question
"92% of GenAI users with employer AI data guidelines still report leaking company data. Guidelines are not runtime control. Runtime control is."
ALLOW
Clean interaction. Full audit record auto-generated request ID, decision log, EU AI Act Art.9 compliance evidence. Every compliant interaction documented without manual effort.
★ UNIQUE
QUARANTINE
Suspicious but not clear-cut. Held in a SOC analyst queue for human review allow, redact, block, or escalate. Grey area gets a human. No other platform does this.
REDACT
Sensitive values stripped from the prompt or response before it continues. User receives a sanitized version. Productivity uninterrupted. Data never exposed.
★ UNIQUE
TOKENIZE
Sensitive data replaced with tokens before model contact. SAMI rehydrates real values in the output. The model never saw PII or proprietary data. Full accuracy maintained. Supports GDPR Art.25 privacy-by-design.
BLOCK
Malicious. Stopped immediately. SOC alerted. Autonomous Response Engine notifies all security layers simultaneously TrustGuard tightens, RAG Defender updates, Agentic Security restricts. All within seconds, without a human in the loop.
Why SAMI is different

Classifiers classify. Guardrails guide.
SAMI enforces.

LlamaGuard is a classifier. NeMo is a programmable framework. SAMI is the session-aware control room around them and the only platform that closes its own gaps automatically.

Policy-as-runtime engine ★ UNIQUE
Hard allow/block/transform on every prompt, response, and tool call. Not a score. Not a flag. A decision. Auditable. Traceable. Enforceable by design. No developer required to update policy.
Autonomous tightening + simultaneous SOC alert ★ UNIQUE
When bypass is detected, policy tightens AND a SOC alert fires at the same moment not sequentially. Simultaneously. NeMo, LlamaGuard, Azure Content Safety: none can do this. SAMI is stronger before the attacker can try again.
Session-level pattern analysis ★ UNIQUE
Multi-step erosion detected across the full conversation. 20 individually benign messages that collectively cross the policy boundary caught before breach. Static guardrails evaluate per-message and miss this entirely.
Policy bundle lifecycle ★ UNIQUE
Draft → validated → approved → published. Model updates and policy updates are fully decoupled. Ship new model versions without touching guardrail code. Rollback in seconds.
Multi-tenant policy isolation
Each team or customer runs isolated policy bundles on shared infrastructure. No code forks. No policy bleed between tenants. Department-specific AI boundaries enforced without separate deployments.
Dual approval with separation of duties
High-risk overrides require a second approver. Self-approval blocked by policy. Exception governance built into the product not bolted on as a process nobody follows.
Audit-grade decision telemetry
Every runtime decision logged with request ID, component-level decisions, reason metadata, and tenant context. When your regulator asks what your AI did and under EU AI Act Article 9, they will, this is the record that answers it. Mean-time-to-investigate: minutes, not hours.
Continuous Gap Assessment

Don't know what guardrails you're missing?
SAMI finds them, implements them, and keeps them current.

Most teams write guardrails once, at deployment, then never update them. SAMI runs a continuous gap assessment against your live AI stack and closes the gaps automatically. Not periodically. Not on a schedule. Continuously, at runtime.

1
Audit current guardrails
SAMI scans your existing guardrail configuration against your AI stack, attack surface, and threat profile. Every gap found including guardrails you never knew you needed.
2
Recommend what's missing
A specific, prioritized list with the attack each missing control closes and the financial exposure without it. Not a generic checklist.
3
Auto-implement on approval
Your team reviews and approves. SAMI implements automatically no developer work, no model redeployment, no downtime. Policy-as-runtime updates instantly.
4
Self-tighten continuously
Guardrails update with live attack patterns, new threat intelligence, and policy changes. When bypass is detected, SAMI auto-tightens and fires a SOC alert simultaneously. No quarterly reviews.
WHY THIS MATTERS

92% of GenAI users with employer AI data guidelines still leaked company data. Your AI stack has changed since you wrote your guardrails. New attack techniques have been published. New models have been deployed. New compliance obligations have come into effect. Your guardrails don't know any of this, unless SAMI tells them.

The question isn't whether you have guardrails. It's whether they're still protecting you today.

SOC & SIEM connection

Your SOC can see a port scan.
It cannot see a prompt injection, unless you give it SAMI.

Most SIEMs see API traffic. They cannot tell you whether the prompt was malicious, whether the response violated policy, or whether a session slowly eroded the AI's guardrails over forty turns. To your security team, AI attacks look exactly like normal user activity.

SAMI creates structured AI security events and sends them directly into your SIEM or SOC workflow. AI incidents are no longer invisible, they land in the same queue your team already works from, with no new workflow and no retraining.

Some AI incident record includes
Request ID
User & session context
Risk classification
Rule triggered
Policy decision
Data type detected
RAG source involved
Agent / tool involved
Action taken
Reason code
Evidence package
Recommended next step
Your SOC sees what happened. Compliance sees which rule applied. Privacy sees what data was protected. Audit has the evidence. Leadership sees that AI risk was controlled.
What changes

The same AI assistant.
Two very different outcomes.

Without SAMI
🔴User bypasses the guardrail, it only blocks the obvious phrasing. Subtle reframes and role-play get through.
🔴Employee pastes customer records into the prompt. The AI processes real PII. Nobody sees it.
🔴A conversation walks the AI outside policy over twenty messages. No session memory. No catch.
🔴Model updated. Guardrails not recalibrated. Behavior that was safe on v1 is now unsafe on v2 silently.
🔴New compliance obligation published. Gap exists for weeks until the next manual audit catches it.
🔴Compliance requires human review before the AI responds. AI answers anyway. The rule was never enforced at runtime.
🔴SOC sees normal traffic. Audit has no evidence. The business doesn't know what its AI did today.
With SAMI
✅Intent evaluated, not just phrasing. Reframes, role-play, and token smuggling caught before the model sees them.
✅Sensitive data tokenized before model contact. The AI processes tokens, not real values. PII never exposed.
✅Session memory holds 20+ turns. Slow erosion caught at message five, not after the breach at message twenty.
✅Drift detected on model update. ARE recalibrates guardrails automatically on approval. No gap between versions.
✅New compliance obligation detected. ARE runs gap analysis across all AI systems. Affected layers notified immediately.
✅Compliance rule enforced at runtime. Human review triggered before the AI responds. The rule runs, it doesn't just exist.
✅Every event creates a structured SOC incident. Security sees it live. Audit has the evidence trail. Leadership has proof.
See what SAMI finds in your AI environment.
Free assessment. No code changes.
Home › CTEM Platform
Continuous Threat Exposure Management

Your scanner found 3,200 things.
Here's why that number is useless.

Finding risk is the easy part. Knowing which three matter this week, what they cost in dollars, and closing them before anyone else finds them that's SAMI. Meanwhile every day those findings sit unaddressed, your liability compounds.

Validated in production CMiC Global & Salesloft, Feb 2025

"97% of SAMI's findings were confirmed true positives. A leading external risk platform returned ~35% on the same assets." That 62-point gap is what your team spends their week on while real vulnerabilities accumulate compounding liability.

AI security command center protecting live AI workflows
The gap most teams don't see until the breach, the audit, or the insurance renewal
✗3,200 findings. No way to know which three are actively exploitable today.
✓Attack simulation validates every finding. 73% is noise. Three advance the right three.
✗CVSS 7.8 doesn't say whether that finding costs €50K or €5M when exploited.
✓FLE gives you the dollar number. SPI ranks by business impact, not a generic score.
✗800 open Jira tickets. Nobody knows which sprint owns them or what done means.
✓SAMI creates the ticket pre-filled with fix steps and ROI, and manages it to closure.
The SAMI Continuous Risk Loop

From first asset to closed ticket to board proof.
Seven stages. Fully automated. Continuously.

This is the process your team runs manually today across 10–15 disconnected tools. SAMI runs all seven stages end-to-end, automatically. Click any stage.

SCOPING Stage 1 DISCOVERY Stage 2 VALIDATION Stage 3 BIA & PRIORITY Stage 4 REM. MODELLING Stage 5 ORCHESTR’N Stage 6 REPORTING Stage 7 SAMI RISK LOOP

↑ Click any segment to explore

One platform. All seven stages. No gaps.

Most organizations cover stages 1–2 with scanners, maybe stage 3 with an annual pen test, then nothing. No dollar liability. No fix planning. No proof it worked. SAMI runs all seven stages continuously connected, automated, end to end.

Without SAMI: 10–15 tools, each covering 1–2 stages. Nothing connected. No dollars. No closure.
With SAMI: All 7 stages. One platform. Continuous. Automated from first finding to board proof.
Attack surfaces 13 covered

Every surface attackers use.
Including the ones added last Tuesday.

Click any surface to see what SAMI finds, what attackers do there, a real recent incident and what changes when SAMI is covering it.

Endpoints Windows, Linux, macOS
Every device is a door. Most have one left unlocked.
▶

What SAMI does: Continuously assesses every managed endpoint against CIS benchmarks. Finds misconfiguration, unpatched software, and privilege escalation paths before attackers do.

When you feel the pain without it: Your EDR fires after the breach. Your patch management tool tells you what's missing not whether it's exploitable in your environment. SAMI validates exploitability and assigns FLE so you know which missing patch costs €800K if exploited, and which can wait.

With SAMI: Every endpoint finding carries a dollar value. Your team works on endpoints that matter, not endpoints that scored highest on a CVSS calculator.

Recent incident: MGM Resorts (2023) a 10-minute social engineering call gave attackers Active Directory access. Endpoint and identity misconfiguration mapped to attack paths SAMI surfaces continuously.
Network & Firewall Attack Simulation
Everyone checks firewall policies. Almost nobody tests if they actually work.
▶

What SAMI does: Runs continuous attack simulation against your network controls and firewall rules not just configuration checks. Tests whether your firewall can withstand the specific techniques being used against organizations like yours today.

When you feel the pain without it: Your firewall policy review says "compliant." Then an attacker uses a technique your policy doesn't cover, and you find out months later. Policy review ≠ attack resistance. SAMI runs the attack if it gets through, you know before the attacker does.

With SAMI: Your firewall is validated against live attack techniques, not just checked for correct configuration. Network segmentation tested. Lateral movement paths mapped. FLE assigned per gap.

Recent incident: Volt Typhoon (2023-24) Chinese state actors lived inside US critical infrastructure networks for months, undetected, by exploiting misconfigured network devices. Policy compliance and attack resistance are not the same thing.
Wi-Fi Security
51 active checks. PCAP forensics. The surface IT forgets at renewal time.
▶

What SAMI does: 51 active wireless security checks plus PCAP forensic analysis. Detects rogue access points, weak encryption, Evil Twin attacks, and authentication vulnerabilities across your wireless estate.

When you feel the pain without it: Wi-Fi security gets reviewed at implementation and then never revisited. A rogue access point running for 6 months is invisible until someone trips over the cable. SAMI continuously monitors your wireless perimeter not just at audit time.

With SAMI: Every wireless finding carries FLE scoring. Rogue APs detected within hours, not quarters. PCAP evidence available for forensic investigation.

Third-Party & External DAST, Pen Testing, Exposed Products
Your external surface is what attackers see. SAMI sees it first.
▶

What SAMI does: Continuous DAST against your externally-facing applications. Automated pen testing updated as new techniques are published. External attack surface mapping covering domains, subdomains, exposed APIs, shadow assets, and externally-facing products. IoT/OT device exposure analysis. External-facing AI system assessment. WAF validation against 200+ bypass techniques.

When you feel the pain without it: Your annual pen test covers what existed 11 months ago. Your WAF report says "rules active" not "rules effective." An externally-facing API deployed by a dev team three weeks ago has no security assessment. SAMI covers your external surface as it exists today, not as it existed last year.

With SAMI: Every new external asset assessed within 24 hours of discovery. Pen testing runs continuously, not annually. WAF validated against live bypass techniques. IoT and OT exposure mapped with FLE per finding.

Recent incident: MOVEit Transfer (2023) zero-day in an externally-facing file transfer application led to hundreds of breaches across industries. Continuous external surface monitoring catches new deployments before attackers find them.
Identity & Access Management (IAM)
Identity is the new perimeter. It's also the most misconfigured one.
▶

What SAMI does: Continuously assesses IAM configuration across your environment Entra ID / Azure AD, AWS IAM, GCP IAM, and on-premises Active Directory. Maps privilege escalation paths, over-permissioned roles, stale accounts, and MFA bypass vectors. Identifies attack paths from any identity to your most critical assets.

When you feel the pain without it: Your IAM review is annual. Accounts accumulate permissions over time no one removes what's no longer needed. A stale service account with Domain Admin rights is invisible until an attacker uses it. SAMI continuously maps identity risk and assigns FLE per path.

With SAMI: Every privilege escalation path carries a dollar liability. Stale accounts flagged within hours of becoming risk. Crown jewel access paths mapped continuously not discovered during the forensic investigation.

Recent incident: MGM Resorts (2023) Scattered Spider used vishing to get Okta credentials, then moved laterally through overpermissioned identities. SAMI's IAM CTEM maps exactly these privilege escalation paths continuously.
M365 & Google Workspace
Where your data lives. Where attackers go first.
▶

What SAMI does: Continuously assesses M365 and Google Workspace configuration. Entra ID attack paths, Exchange mail flow rules with exfiltration potential, Teams guest access misconfigurations, SharePoint over-sharing, OAuth app permissions, and conditional access gaps.

When you feel the pain without it: Microsoft Secure Score tells you what Microsoft recommends. It doesn't tell you which misconfiguration an attacker can exploit today. A Secure Score of 68 can coexist with three confirmed Entra ID attack paths. It does, regularly.

With SAMI: Every M365 and Google Workspace finding validated for exploitability. FLE per path. Remediation guidance specific to your configuration not generic Microsoft documentation.

Recent incident: Microsoft Exchange Online breach (2023) attackers used a stolen MSA signing key to forge tokens and access cloud email across multiple organizations. SAMI's M365 CTEM covers token and authentication risk continuously.
Cloud AWS, Azure, GCP
Every change creates a new configuration. SAMI retests within 24 hours.
▶

What SAMI does: Continuous cloud configuration assessment across AWS, Azure, and GCP. Detects misconfiguration, over-permissioned roles, exposed storage, and insecure defaults. Integrates with your existing cloud security tooling. Cloud findings produce IaC-ready remediation output (Terraform/Bicep).

When you feel the pain without it: A developer spins up a new resource with a public IP. A security group widens for testing and never tightens back. Your quarterly cloud audit finds it months later after it's been an active attack vector. SAMI detects every change within 24 hours and validates exploitability before it becomes a breach.

With SAMI: Cloud drift caught in hours, not quarters. Every misconfiguration carries FLE. IaC-ready remediation means DevOps can fix it in the same sprint it's found.

Recent incident: Capital One (2019) misconfigured AWS WAF and overpermissioned EC2 role exposed 100M+ records. Cloud misconfiguration remains the leading cause of cloud breaches. SAMI catches drift before attackers do.
OT / IoT / ICS / SCADA
27+ industrial protocols. The surface that can't go offline to be patched.
▶

What SAMI does: Assesses OT/IoT/ICS environments without disrupting operations. Supports 27+ industrial protocols. Maps every connected device, identifies legacy vulnerabilities, and produces FLE-scored findings your engineering and risk teams can actually act on.

When you feel the pain without it: OT assessed in isolation, separate tools, separate reports. A PLC finding scored against OT benchmarks in isolation looks different from the same finding scored in the context of your full risk picture adjacent to IT attack paths, connected to production systems, with regulatory exposure. Context changes priority completely.

When you feel the pain without it: OT security is assessed in isolation a separate team, separate tools, separate report that never connects to your broader risk picture. A vulnerable Siemens PLC assessed in isolation scores differently than the same finding in context connected to production systems, with OT-specific regulatory exposure, adjacent to IT systems with active exploits. SAMI maps OT risk in the same FLE/SPI framework as everything else. Context changes the priority completely.

With SAMI: OT/IoT findings in the same SPI/BRI scoring framework as all other surfaces. Board report includes OT liability. Remediation orchestrated alongside IT findings.

Recent incident: Oldsmar water treatment plant (2021) attacker remotely increased sodium hydroxide levels 111× via an exposed remote access tool. ICS security assessment is a core SAMI CTEM surface.
Applications Custom, Vibe-Coded & Supply Chain
The fastest-growing attack surface. The one most security teams aren't ready for.
▶

What SAMI does: SAST, DAST, API security, and dependency scanning for custom-built applications. Specific assessment of vibe-coded apps AI-assisted development where developers accept LLM-generated code without full review, introducing insecure defaults and exposed credentials. Software composition analysis and SBOM risk for third-party and supply chain dependencies.

When you feel the pain without it: A developer ships a feature built with Copilot in two hours. Nobody reviewed the LLM-generated authentication logic. Your DAST scanner wasn't updated to cover that new endpoint. Three months later, it's a breach. SAMI covers apps as they're shipped, not 6 months after.

With SAMI: Every new deployment assessed. Vibe-coded code scanned for AI-specific risk patterns. Supply chain dependencies mapped to live CVEs with FLE per finding.

Recent incident: XZ Utils backdoor (2024) a malicious contributor spent two years building trust before embedding a backdoor in a widely-used compression library. SAMI's supply chain CTEM identifies dependency risk before exploitation.
AI, LLM, MCP & Agentic Security
OWASP Top 10 for LLM Applications 2025 · MITRE ATLAS · The surface your WAF can't see.
▶

What SAMI does: Continuously assesses your AI deployment against all OWASP LLM Top 10 categories and MITRE ATLAS AI attack patterns. LLM endpoint vulnerability assessment, MCP tool permission analysis, agentic chain blast radius simulation. Assigns FLE per finding. Re-assesses within 24 hours of every model or pipeline change.

When you feel the pain without it: AI vulnerabilities don't look like vulnerabilities. No malware, no unusual ports. Prompt injection, RAG poisoning, and agent hijacking appear as normal API calls because they are. WAF, SIEM, and EDR need AI context to judge prompt intent, retrieved-content risk, and agent action scope. You have no idea they're happening until the data is gone.

With SAMI: Every AI interaction inspected in real time. Every AI surface assessed continuously. Complete audit trail: who sent the prompt, what RAG data was retrieved, what the agent did, what the model returned.

Recent incident: Slack AI (Aug 2024) attackers injected instructions into documents retrieved by Slack's AI assistant, exfiltrating private messages from other users. Covered by SAMI RAG Defender at ingestion-time and query-time.
How SAMI compares

Other tools find risk.
SAMI is the only one that closes it.

The differences determine whether your team spends the week closing real risk or validating false positives.

97%
SAMI true positive rate
CMiC Global & Salesloft, Feb 2025
~35%
Black Kite / SecurityScorecard
Same assets · Same timeframe · Validated
73%
Scanner noise eliminated
Before any finding reaches your team
Capability SAMI Tenable
Nessus/One		 Qualys
VMDR		 Black Duck
Synopsys		 Security
Scorecard		 Black Kite
Discovery & coverage
Vulnerability scanning✓✓✓Partial✓✓
Network & firewall attack simulation✓
External attack surface + pen testing (continuous)✓PartialPartial✓✓
IAM / identity attack path mapping✓PartialPartial
M365 / Google Workspace misconfiguration✓
OT / IoT / ICS / SCADA✓PartialPartial
LLM / AI / MCP / Agentic security✓
Supply chain / SBOM / vibe-coded app risk✓✓PartialPartial
Validation & signal quality
Attack simulation validates exploitability (not just existence)✓
True positive rate (production validated)97%~65%~65%~70%~35%~35%
Business impact & remediation
Dollar liability per vulnerability (FLE)✓
Business-context prioritisation replacing CVSS (SPI)✓
Remediation orchestration tickets managed to closure✓
EU AI Act Article 9 continuous evidence✓
Based on public documentation Q1 2026 and production validation data (CMiC Global & Salesloft, Autnhive Feb 2025). Claims should be independently verified.
See what SAMI finds in CTEM.
Free assessment. No code changes.
Home › Liability Engine
CTEM Liability Engine · HIVE Layer · Financial Intelligence

Your CFO will ask this question.
Do you have the answer?

The question is coming. It might be from your board after they read about the EU AI Act fine. It might be from your CFO during the cyber insurance renewal. It might be from your insurer asking why your premium went up 40%. "What is our AI security risk exposure in dollars?" SAMI is the only platform that answers it.

The question that ends careers

"We had a breach. Our AI was exploited. The fine is €8.4 million. The insurer won't pay because we had no documented risk management. Why didn't anyone flag this?"

📊 Run Liability Calculator See Remediation Loop →
SAMI · Liability Calculator
The Liability Engine FLE · SPI · BRI · SSVC

Risk, in the language
every stakeholder understands.

Four proprietary metrics that turn vulnerability data into financial decisions.

Step 1
FLE
Financial Loss Enumeration

Dollar liability calculated per individual vulnerability. Regulatory fines + data breach cost + operational disruption + insurance impact + litigation.

Step 2
SPI
Strategic Prioritization Index

FLE + cyber-risk score + asset criticality + attack frequency + compliance exposure + operational disruption. One fix-first number, customised to your business.

Step 3
BRI
Breach Reduction Index

Quantifies how much each remediation action reduces overall breach likelihood and financial exposure. BRI 0 = fully protected. BRI 100 = maximum exposure.

Step 4
SSVC
Modified SSVC Decision

BRI feeds Modified SSVC model: Fix Now · Schedule · Defer · Accept. Plain language decisions for CISO, CFO, and board. No security degree required.

🔍
Stage 1
BRI Score
Exposure in dollars
🎯
Stage 2
SPI Priority
Fix-first order
📈
Stage 3
Financial Model
P50 / P75 / P95
🏦
Stage 4
Insurance Delta
Premium reduction
Stage 1 BRI

Breach Reduction Index.
Risk in dollars.

CVSS measures how hard a vulnerability is to exploit technically. BRI measures how much it costs you when exploited. A CVSS 4.2 Medium on your customer-facing AI with an active exploit and €1M regulatory exposure is BRI Critical. CVSS would have you fix it last.

BRI 76–100 Critical exposure
Fix Now
BRI 50–75 Elevated risk
Schedule
BRI 25–49 Managed risk
Defer
BRI 0–24 Acceptable
Accept
QuestionCVSSBRI (SAMI)
What does it measure?Technical severityFinancial liability ($)
Asset value considered?✗✓
Live threat intel?✗✓
Regulatory fines included?✗✓
Insurance impact shown?✗✓
Fix-first order provided?✗✓ via SPI
Board understands it?✗✓
⚠ Regulatory Reality · EU AI Act · Article 9
EU AI Act Article 9: high-risk AI now needs lifecycle risk evidence
The risk is not only a breach. Article 9 requires documented, lifecycle risk management for high-risk AI systems - a process SAMI turns into live evidence. Security tools are not enough if the organization cannot prove risk was continuously monitored, reviewed, and acted on.
"If the EU AI Office audited your high-risk AI systems tomorrow, could you produce the Article 9 risk management documentation?"
Every finding SAMI blocks carries a BRI impact score showing the financial liability reduction that specific protection achieved. Your board finally sees what security is worth.
CF
CFO
Regulated Financial Services · EU Operations
Stage 2 SPI

Strategic Priority Index.
One queue. No debate.

SPI combines 6 weighted factors into one fix-first number. Click any finding below to see the breakdown.

RAG Poisoning Customer AI
91
CVSS 4.2 MediumAI Security
Prompt injection in customer knowledge base. Executes on every retrieval. Passes all static guardrails silently.
SMBv1 Internal Server
28
CVSS 7.8 HighEndpoint
Legacy protocol enabled on internal server. No external exposure. No active exploitation in this environment.
Shadow AI Processing PHI
79
CVSS 3.1 LowAI Security
Undisclosed LLM API integrated by developer, processing support tickets with PII. EU AI Act Art.13 breach.
M365 Over-Permission Finance
63
CVSS 5.5 MediumM365
14 guest accounts with unreviewed access to financial SharePoint. Active accounts, 6 months old. GDPR Art.25 gap.
The insight: CVSS ranks these: SMBv1 (7.8 High) → M365 (5.5 Med) → RAG (4.2 Med) → Shadow AI (3.1 Low). SPI ranks them: RAG (91) → Shadow AI (79) → M365 (63) → SMBv1 (28). SPI fixes what matters. CVSS fixes what scores highest.
SPI Score
91
Strategic Priority Index
Score Components
Recommended Action
🔴 Fix Now
Customer-facing AI + active exploit + EU AI Act Art.9 = maximum liability. CVSS says "fix later". SPI says fix first.
BRI Financial Exposure
$1.2M
Regulatory Exposure

The fines are real.
The clocks are running.

SAMI automatically quantifies your regulatory penalty exposure across every relevant framework per AI system, per regulation.

🇪🇺
EU AI Act
Article 9 Exposure
Regulation (EU) 2024/1689 in force now, full enforcement August 2026. Articles 9, 13, and 17 create risk-management, transparency, and quality-management duties for high-risk AI systems. Article 9 requires documented lifecycle risk management.
✓ SAMI automates Art.9 risk management, Art.13 transparency, Art.17 quality management proof
🏥
HIPAA + AI
$1.9M
Per violation, per year. OCR is actively investigating AI-related HIPAA breaches. For covered entities and business associates, AI systems that contain or use ePHI, including chatbots, clinical summarisation, and diagnostic AI, must be governed under HIPAA technical safeguards. SAMI tokenizes PHI before the model ever sees it.
✓ SAMI tokenizes PHI pre-model · GDPR Art.25 by design · PHI never processed unencrypted
📈
SEC Cybersecurity Rule
4 Days
Material AI cyber incidents must be disclosed within 4 business days (17 CFR §229.106). AI risk must be detailed in annual 10-K filings. SAMI generates 10-K-ready AI risk documentation automatically no manual documentation required.
✓ SAMI auto-generates 10-K-ready AI risk reports · Material incident alerts in real time
🇺🇸
Colorado AI Act (SB 205)
Feb 2026
Risk management, bias audits, and consumer disclosures for high-risk AI. Template for 8+ US states. SAMI's continuous monitoring maps to all SB 205 obligations automated gap analysis included.
✓ SAMI maps all SB 205 obligations · Automated gap analysis on every assessment
🏦
FTC Section 5
Active Now
Already enforcing. No new law required. AI-specific guidance active since 2023. Deceptive AI outputs constitute an unfair commercial practice. SAMI prevents unauthorized outputs at the model boundary before they reach the user.
✓ SAMI prevents deceptive outputs · AI Firewall intercepts before response leaves model
⚖️
US AI Litigation
No Deadline
Courts are establishing the standard of care for AI security right now. Air Canada shows legal liability for AI-provided misinformation. Samsung shows how sensitive IP can leak into public AI tools during normal work. No deadline: that is what makes it the most dangerous exposure. Without documented, continuous monitoring, every AI incident is a liability event.
✓ SAMI provides continuous documented monitoring · Legal-grade audit trail on every assessment

Know your number.
Before the board asks.

A 48-hour SAMI assessment delivers a complete P50/P75/P95 liability model from your actual environment not estimates. Board-ready PDF. Insurer-grade methodology.

📊
Full Liability Assessment
48-hour assessment. Board-ready P50/P75/P95 model. Regulatory exposure per regulation. Insurance premium delta.
Try it out · Real environment · Board-ready PDF
Get Liability Report →
🔄
See Remediation Loop
See how SAMI models liability reduction before you fix then proves it after.
🎯 The loop no competitor closes
Remediation Orchestration →
Home › Financial Liability
P50 · P75 · P95 · Actuarial Modelling

The number your
board and insurer
both need.

SAMI quantifies your breach liability in the same actuarial terms your cyber insurer uses. P50, P75, P95 per AI system, per regulation, per surface. Financial clarity that drives decisions.

Calculate My Exposure → See Remediation →
The Killer Discovery Question
"What is your P95 breach liability for your three most critical AI systems and does your cyber insurance cover it?"
Sample AI System Financial Services
P50 Probable Annual Loss
$2.4M
50% probability breach costs this or less
P75 Elevated Scenario
$3.9M
Upper-quartile breach cost
P95 Catastrophic (tail risk)
$8.2M
Includes regulatory fines and litigation
With SAMI
$0.96M
P50 with SAMI deployed 60% reduction
Liability Calculator

Calculate your
actual exposure.

Breach Liability Calculator P50 / P75 / P95
Your Breach Liability Estimate
P50: Probable Annual Loss
Select options →
P75: Elevated Scenario
P95: Catastrophic tail risk
With SAMI, Estimated P50 Reduction

Share with your board, insurer, or legal team.

Get Full Financial Report →

Know your number.
Before your insurer does.

💰
Free Exposure Assessment
We calculate your real P50/P75/P95 across all your AI systems and CTEM surfaces.
Try it out · Zero commitment
Get Free Assessment →
📋
Board-Ready Report
Full financial liability model. Regulatory exposure quantified. Insurance premium delta calculated.
Included with Proof of Value
Book PoV →
Home › Remediation Orchestration
The Loop No Competitor Closes

Finding risks fills dashboards.
Fixing the right ones changes outcomes.

Your security team closed 240 tickets last quarter. How many of those actually reduced your financial liability? Which three would have mattered most if attackers had arrived last Tuesday? Without SAMI, nobody knows. With SAMI, everybody does before a single hour is spent.

The remediation backlog problem

The average enterprise security team has 1,400 open findings. They close about 60 per month. At that rate, they will never catch up. The question is not "how do we close more tickets?" The question is "which 6 tickets, if closed this week, would reduce our liability by 80%?"

The Question SAMI Answers That Others Don't

"Are you fixing what reduces real liability the most and can you prove it worked?"

Remediation Before vs After SAMI
Without SAMI
Long backlog nobody trusts
CVSS scores drive priority
No financial context per fix
IT and security argue weekly
Fixes never re-validated
No board-ready proof
With SAMI
SPI-ordered queue no debate
Liability reduction per fix shown
Fix modelled before assigned
Right team, right ticket, auto
Re-validation confirms it worked
BRI reduction in board PDF
70%
Faster remediation first quarter
100%
Fixes re-validated by SAMI
0
Board debates on priority
The 6-Step Loop

Model. Assign. Track.
Validate. Prove.

Click each step to see exactly what SAMI does and what no other platform does.

Step 01
Validated Finding
Attack-confirmed only
Step 02
Risk Business Impact Assessment & Prioritization
Fix-first order
Step 03
Remediation Modelling
Liability delta before fix
Step 04
Remediation Orchestration
Right team, right tool
Step 05
Track & SLA
Live visibility
Step 06
Re-Validate
Prove it worked
Step 01: Validated Finding
SAMI only advances findings that have been confirmed exploitable by attack simulation. Before any remediation effort is spent, SAMI has already proved an attacker can actually reach and exploit the vulnerability in your specific environment. This single step eliminates an average of 73% of typical scanner noise ensuring every remediation ticket represents a real, confirmed risk.
Noise Reduction
73% avg
False Positive Rate
< 2%
Time to Validate
< 4 min
LIVE
What Makes SAMI Different

Model liability reduction
before you fix.

Before any fix is assigned, SAMI models exactly how much that fix will reduce your BRI score and financial liability. Your CISO sees the liability delta. Your CFO sees the ROI. Your board sees the risk curve moving down before a single engineering hour is spent.

This is why SAMI is a risk execution platform, not a detection platform. Detection tells you what's wrong. SAMI tells you what fixing it is worth.

"We reduced remediation timelines by 70% in the first quarter. SAMI didn't just find the risk it told us what to fix first and proved it worked."

VP Security, Global Enterprise
Example Before Fix is Assigned
Finding
RAG Poisoning Customer AI (SPI 91)
Current BRI Exposure
$1.2M
↓ SAMI models fix impact ↓
BRI After Fix (modelled)
$0 Fully eliminated
Shown to CISO Before Assignment
Fix cost: ~4 engineering hours
Liability reduction: $1.2M
ROI: 300:1 minimum
Integrations

Fits your existing
workflow exactly.

SAMI doesn't ask your team to change tools. It enriches the tools they already use.

Jira / ServiceNow
Validated findings become tickets automatically. SPI score, financial liability, effort estimate, and fix guidance in every ticket body.
SIEM / SOAR
AI attacks feed as first-class incidents. OWASP LLM IDs, SPI scores, and liability data included. SOC works exactly as before just with full AI visibility.
Slack / Teams
Real-time remediation updates, SLA alerts, and re-validation confirmations via existing channels. No new notification fatigue.
Coming Soon...
CI/CD Pipelines
Security findings in your DevOps workflow. Block deployments on Critical SPI findings. AI security gates in your pipeline.
Terraform / IaC
Cloud findings produce IaC-ready remediation code. Handed directly to DevOps no manual translation, no rewriting.
Compliance Platforms
EU AI Act, HIPAA, ISO 27001, SOC 2 evidence auto-exported. Audit-grade PDF and JSON. Reduces manual GRC work from weeks to minutes.
Insurance Brokers
P50/P75/P95 liability model plus SAMI coverage summary generated on demand. Insurer-grade format. Premium negotiation evidence.
Board Reporting
One-click board PDF: BRI trend, SPI queue, liability reduction achieved, regulatory exposure, remediation velocity. No manual slides.
Close the loop. Prove it worked.
Free assessment. No code changes.
Home › Newsroom & Whitepapers
Newsroom

AI Security News& SAMI Updates.

Real-world incidents. Platform announcements. Threat intelligence. Regulatory developments. The AI security landscape changes daily SAMI tracks what matters.

Autnhive announces "SAMI for AI" at the 2026 World Defense Show in Riyadh, Saudi Arabia. Autnhive's breakthrough security platform for AI systems - SAMI for AI, the world's most comprehensive security platform for AI systems, was recently announced by Devi Narayan, Founder & CEO of Autnhive. SAMI for AI was extremely well received by industry leaders and governments alike, all who are desperate to secure their AI systems.

All
Press Releases
White Papers
Home › Whitepapers & Research
Research & Intelligence

The threats attackers know.
Your board doesn't yet.

In Q1 2026, Autnhive Labs documented 847 active AI attack templates in circulation, a new RAG poisoning variant bypassing NeMo with a high bypass rate observed in Autnhive Labs retrieval-layer testing, and a coordinated multi-step erosion campaign targeting enterprise AI deployments. None of it was in the mainstream security press. All of it is in here free, no hard sell.

What your security team doesn't know yet

The multi-step erosion technique that bypasses AI safety policies across 6 sessions with no single detectable violation was documented by Autnhive Labs in February 2026. It is being used in active campaigns now. The first time most security teams will hear about it is when they read this report.

The question to ask your security team today

"What AI attack techniques are being actively used against companies like ours right now and how long before we find out the hard way?"

📥 Download Any Paper Free
Stay ahead of the
threat landscape.

Autnhive Labs publishes new research monthly. New threat intelligence, technique breakdowns, and regulatory analysis sent to your inbox no marketing, no noise.

No spam. Unsubscribe any time. Research only.

Prefer a custom
briefing?

Our team can present any of these papers as a live briefing for your board, security team, or legal and compliance function with your environment data where relevant.

🎤
Board Briefing
Live presentation of AI security threat landscape, liability exposure, and SAMI's approach tailored to your industry and environment.
60–90 mins · Remote or on-site
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🔬
Technical Deep Dive
Technical briefing for security, DevOps, and platform engineering teams covers threat mechanics, detection methods, and integration architecture.
90 mins · Technical audience
Request Technical Briefing →
Home › Trust & Compliance
Trust & Compliance

EU AI Act Article 9 requires proof.
SAMI keeps that proof live.

Article 9 of the EU AI Act requires documented, lifecycle risk management for high-risk AI systems, evidence that risks are identified, monitored, mitigated, and reviewed over time. SAMI automates the evidence trail from day one: risk events, decisions, remediation actions, and audit-ready exports.

EU AI Act · Article 9

"If the EU AI Office audited your high-risk AI systems tomorrow, do you have documented continuous risk management evidence ready to produce?"

HIPAA + AI · OCR Guidance

"Which AI systems in your environment are processing protected health information and can you prove that PHI is never stored unencrypted in model context?"

Regulatory Clocks

The regulations that
require SAMI.

Every one of these frameworks requires documented, continuous AI risk management. Manual spreadsheets fail all of them.

🇪🇺
EU AI Act - Phased Enforcement
First fine already issued: €8.4M · March 2026
Regulation (EU) 2024/1689 is in force and applies in phases, with most high-risk AI obligations applying from August 2026.. Articles 9 (risk management), 13 (transparency), and 17 (quality management) require documented, continuous monitoring for every high-risk AI system. Enforcement risk is evidence-based: organizations should maintain documented lifecycle risk management, testing, monitoring, incident handling, and governance records. Penalties can reach €35M or 7% of worldwide annual turnover for prohibited AI practices, and €15M or 3% for many operator obligations.
✓ SAMI automates Art.9 risk management, Art.13 transparency logs, Art.17 QMS proof continuously
🇺🇸
Colorado AI Act (SB 205)
Active Since 1 Feb 2026
Risk management obligations, bias audits, and consumer disclosures for high-risk AI systems. Template legislation being adopted by 8+ US states. SAMI's continuous monitoring and gap analysis maps to all SB 205 obligations automatically.
✓ SAMI generates SB 205 compliance evidence automatically · Risk analysis on every assessment
SEC Cybersecurity Rule
Active Since 2023
Material AI cyber incidents must be disclosed within 4 business days (17 CFR §229.106). AI risk must be detailed in annual 10-K filings. SAMI generates 10-K-ready AI risk documentation and flags material incidents in real time.
✓ SAMI auto-generates 10-K AI risk documentation · Material incident alert within minutes
HIPAA + AI (OCR Guidance)
Actively Enforced
HHS OCR confirmed AI systems processing PHI are subject to full HIPAA Technical Safeguards. HIPAA civil penalties are inflation-adjusted and can reach multi-million-dollar annual caps. SAMI tokenizes PHI before any AI model processes it PHI never appears in model context, logs, or outputs.
✓ PHI tokenization pre-model · Supports HIPAA Technical Safeguards with audit controls, access evidence, and PHI protection · BAA available
FTC Section 5
Active Now
Already enforcing. No new law required. AI-specific guidance in force since 2023. Deceptive AI outputs constitute an unfair commercial practice. SAMI prevents unauthorized and hallucinated outputs at the model boundary before users see them.
✓ SAMI AI Firewall intercepts outbound responses · Prevents deceptive outputs before delivery
US AI Litigation
No Deadline
Courts are establishing the standard of care for AI right now. Air Canada liability ruling (2024) has been cited in 3 subsequent cases establishing that organizations are responsible for AI outputs. Without documented, continuous monitoring, every AI incident is a liability event.
✓ SAMI provides legal-grade audit trail · Continuous documented monitoring · Evidence on demand
Compliance Coverage Matrix

How SAMI covers
every framework.

Regulation Actual Requirements Explanation SAMI Coverage
EU AI Act Article 9 Article 9 requires providers of high-risk AI systems to establish, implement, document, and maintain a continuous risk management system throughout the entire AI lifecycle. Requires organizations to continuously manage and monitor AI risks instead of treating risk assessment as a one-time activity. The regulation expects measurable governance and evidence of ongoing risk control. HIVE CTEM provides the continuous risk management loop, documented, maintained,auditable. Board PDF auto-generated.
EU AI Act Article 13 Article 13 requires high-risk AI systems to be designed and developed with sufficient transparency to enable users to interpret outputs and use the system appropriately. Requires organizations to ensure users and auditors can understand how AI systems function, how outputs are generated, and how decisions can be traced for governance and compliance purposes. Workflow Intelligence provides transparency and traceability for every high-risk AI interaction.
GDPR Article 25 Article 25 requires Data Protection by Design and by Default, ensuring appropriate technical and organizational measures are implemented to protect personal data during processing. Requires privacy and security controls to be embedded directly into systems and AI workflows so that unnecessary personal data exposure is prevented by default. L05 tokenizes PII before model contact, the model never processes real personal data. Structural, not detection policy.
GDPR Article 30 Article 30 requires Records of Processing Activities (ROPA), including purposes, categories, transfers, recipients, retention periods, and safeguards related to personal data processing. Requires organizations to maintain auditable records showing how personal data is processed, accessed, transferred, retained, and secured across systems and workflows. Workflow Intelligence provides detailed audit trails of AI interactions, including data access events, applied policies, workflow activities, and processing evidence that support GDPR Article 30 record-keeping and audit requirements.
GDPR Article 32 Article 32 requires controllers and processors to implement appropriate technical and organizational measures to ensure a level of security appropriate to the risk associated with processing personal data. Requires organizations to implement risk-based cybersecurity and privacy controls to protect personal data against unauthorized access, loss, alteration, or disclosure. Live layers L01–L05 plus CTEM evidence of continuous risk management.(Physical not applicable, however, Logical Controls are implemented) Technical and Organizational Measures (TOM)
HIPAA 45 CFR 164.312(b) HIPAA 45 CFR 164.312(b) requires implementation of hardware, software, and procedural mechanisms that record and examine activity in systems containing electronic protected health information (ePHI). Requires healthcare organizations and service providers to maintain audit trails and monitoring capabilities for systems handling protected health information. L05 ensures PHI never enters model context or logs. Workflow Intelligence provides the required access log.
OWASP LLM Top 10 2025 OWASP LLM Top 10 identifies the most critical security risks affecting Large Language Model (LLM) applications and AI systems. Provides practical security guidance for securing AI and LLM applications against emerging threats, misuse, and adversarial attacks. All ten vectors addressed across SAMI's five live security layers. Framework auto-mapping per guardrail.
NIST AI RMF The NIST AI Risk Management Framework provides guidance for managing risks associated with trustworthy AI systems throughout their lifecycle. Helps organizations establish structured governance and risk management processes for developing, deploying, and operating trustworthy AI systems. Framework auto-mapping in TrustGuard. Gap analysis identifies obligations not yet covered.
ISO 42001 ISO/IEC 42001 specifies requirements for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System (AIMS). Provides a formal management system framework for governing AI systems responsibly, securely, and compliantly within organizations. Guardrail inventory, decision history, framework mappings, and risk scores in regulatory-grade exports.
SEC Cybersecurity Rules SEC Cybersecurity Rules require public companies to disclose material cybersecurity incidents and describe cybersecurity risk management, governance, and oversight practices. Requires publicly traded organizations to demonstrate executive and board-level cybersecurity governance and timely reporting of material cyber incidents. Structured AI incident records showing what happened, what data/tool was involved, and what control action occurred.
NYDFS 23 NYCRR 500 NYDFS Part 500 requires covered financial institutions to establish and maintain a cybersecurity program designed to protect information systems and nonpublic information. Establishes mandatory cybersecurity requirements for financial institutions operating under the New York Department of Financial Services. AI activity audit trail, risk assessments, vulnerability management, and incident response evidence.
FTC Safeguards Rule The FTC Safeguards Rule requires financial institutions to develop, implement, and maintain a comprehensive information security program to protect customer information. Requires organizations handling consumer financial information to implement safeguards protecting confidentiality and integrity of customer data. AI workflow risk assessment, remediation tracking, control evidence, and service/vendor AI workflow review.
PIPEDA (Canada) PIPEDA requires organizations to protect personal information through appropriate security safeguards and obtain meaningful consent for collection, use, and disclosure of personal data. Governs how private-sector organizations in Canada collect, use, disclose, and protect personal information. Workflow evidence of personal-data protection in AI workflows. CTEM maps personal-data exposure.
Privacy Act / APP 11 (Australia) APP 11 requires organizations to take reasonable steps to protect personal information from misuse, interference, loss, unauthorized access, modification, or disclosure. Requires organizations in Australia to safeguard personal information using reasonable security measures throughout the data lifecycle. Runtime data controls plus CTEM evidence of AI personal-information exposure management.
SOCI Act (Australia) The Security of Critical Infrastructure (SOCI) Act requires operators of critical infrastructure assets to manage and report cyber risks affecting essential services. Strengthens cybersecurity and resilience requirements for organizations operating critical national infrastructure in Australia. Runtime controls and CTEM evidence for AI workflows affecting critical infrastructure.
Saudi PDPL The Saudi Personal Data Protection Law (PDPL) regulates the processing of personal data and requires organizations to protect privacy rights and secure personal information. Establishes privacy and data protection obligations for organizations processing personal data within Saudi Arabia.PDPL also includes consent, lawful basis, rights, and international transfer obligations. Personal-data controls in AI prompts, responses, RAG, agents, and MCP/tool calls. Workflow evidence.
UAE PDPL The UAE Personal Data Protection Law (PDPL) establishes requirements for lawful processing, protection, and transfer of personal data within the UAE. Provides a national privacy framework governing the collection, use, storage, and protection of personal data in the UAE. Workflow evidence showing what data was touched, what policy applied, and how exposure was reduced.
Japan APPI The Act on the Protection of Personal Information (APPI) requires businesses to properly handle and protect personal information while respecting individual privacy rights. Governs personal data protection and privacy obligations for organizations operating in Japan. Runtime controls and Workflow Intelligence audit trail for AI systems processing Japanese personal data.
DORA (EU) The Digital Operational Resilience Act (DORA) requires financial entities to ensure ICT operational resilience and manage cyber risks affecting financial services. Strengthens operational resilience and cybersecurity requirements for financial institutions and ICT providers across the EU. Resilience testing via attack simulation, third-party AI workflow evidence, re-validation, and incident response.
NIS2 Article 21 Article 21 of the NIS2 Directive requires essential and important entities to implement appropriate and proportionate cybersecurity risk management measures. Requires organizations operating critical or important services in the EU to implement strong cybersecurity and resilience measures. AI asset discovery, vulnerability validation, incident evidence, remediation prioritisation, and audit records.
EU AI Act Article99 penalty Article 99 establishes penalties and administrative fines for non-compliance with obligations under the EU AI Act. Defines the financial and regulatory consequences for organizations failing to comply with EU AI Act requirements, including significant monetary penalties for high-risk violations. €35M or 7% of global annual turnover for the most Prohibited Practices.€15M or 3% for most operator obligations.
By Industry

Your industry.
Your regulations.

Healthcare
HIPAA ISO 27001 EU AI Act (medical AI) GDPR Art.25
PHI tokenization, medical AI compliance, HIPAA Technical Safeguards, clinical AI audit trails. BAA available.
Financial Services
SEC 17 CFR §229.106 FTC Section 5 EU AI Act (high-risk) SOC 2 Type II
SEC disclosure automation, FTC AI compliance, financial model protection, insider threat via AI agents, board-ready P95 liability model.
Government & Defense
Zero Telemetry Mode Air-Gap Deployment NIST SP 800-207 MITRE ATLAS
Zero telemetry, air-gap, classified environments. ICS/SCADA security (27+ protocols). Nation-state AI attack detection.
Education
GDPR / FERPA COPPA (student data) ISO 27001
Student data protection, AI tutoring security, M365/Teams for Education, COPPA-compliant AI deployment. Reference customer: Peel District School Board.
Critical Infrastructure
IEC 62443 NIST SP 800-82 CISA Guidelines
OT/ICS/SCADA security (27+ protocols), IEC 62443 compliance, passive-first assessment, air-gap deployment for plant floor environments.
Retail & E-Commerce
PCI-DSS GDPR Art.25 FTC Section 5
Customer AI chatbot security, PCI-DSS Req.11 continuous testing, WAF validation against ecommerce bypass patterns, Air Canada-type liability prevention.
Home › Trust & Compliance
Trust & Compliance

EU AI Act Article 9 requires proof.
SAMI keeps that proof live.

Article 9 of the EU AI Act requires documented, lifecycle risk management for high-risk AI systems, evidence that risks are identified, monitored, mitigated, and reviewed over time. SAMI automates the evidence trail from day one: risk events, decisions, remediation actions, and audit-ready exports.

EU AI Act · Article 9

"If the EU AI Office audited your high-risk AI systems tomorrow, do you have documented continuous risk management evidence ready to produce?"

HIPAA + AI · OCR Guidance

"Which AI systems in your environment are processing protected health information and can you prove that PHI is never stored unencrypted in model context?"

Certifications

Certifications by SAMI
your team looks for.

SAMI holds, or continuously monitors against, every major security, privacy, and AI governance framework. Evidence packages available on request.

Our cyber insurer asked for evidence of continuous AI security monitoring at renewal. Three months earlier, I couldn't have produced it. With SAMI, I generated a 90-day audit trail, the EU AI Act gap analysis, and a P50/P75/P95 model in one afternoon. The premium conversation went completely differently.
CI
CISO
Insurance Technology · Lloyd's Market · London
ISO 27001
Information Security Mgmt
✓ Certified
SAMI is ISO 27001:2022 certified across all scopes platform development, cloud operations, customer data handling, and incident management. Annual surveillance audits with zero findings to date. Certificate available on NDA.
SAMI Coverage
Annex A controls automated where applicable. Continuous monitoring closes the gap between annual audits. Evidence export on demand.
SOC 2 Type II
Security · Availability · Confidentiality
✓ Attested
SAMI completed SOC 2 Type II audit across Security, Availability, Confidentiality, and Privacy Trust Service Criteria zero exceptions. Audit period covers 12 continuous months. Full report available under NDA to qualified prospects.
SAMI Coverage
SOC 2 controls evidenced continuously. SAMI's own audit logging covers the CC6, CC7, CC8, and CC9 criteria automatically.
ISO 42001:2023
AI Management Systems
✓ Evidence/
Readiness Support
SAMI is the first AI security platform globally to achieve ISO 42001 the international standard for AI management systems. Certification covers SAMI's own AI components and the AUTN+HIVE platform architecture. Published February 2026.
SAMI Coverage
SAMI maps customer AI systems to ISO 42001 controls continuously. Gap analysis and evidence export included in every CTEM assessment.
🇪🇺
GDPR
EU Data Protection Regulation
✓ Compliant
SAMI is GDPR compliant across all processing activities. Article 25 (data protection by design) is satisfied through tokenization PII is replaced before AI processing and rehydrated in outputs. DPA available for all customers. Data residency options available.
SAMI Coverage
SAMI's tokenization layer satisfies Art.25. Continuous monitoring generates Art.30 Records of Processing Activities automatically. DPA available on request.
HIPAA
Health Insurance Portability Act
✓ Compliant
SAMI is HIPAA-ready with Business Associate Agreement (BAA) available for covered entities and business associates. PHI tokenization ensures AI models process tokens only PHI never appears in model context or outputs. Technical Safeguards satisfied by design.
SAMI Coverage
PHI tokenization pre-model. BAA available. Audit logs for all PHI access events. Technical Safeguard evidence generated automatically.
US
CCPA/CPRA
California Consumer Privacy Act / California Privacy Rights Act
✓ Compliant
Autnhive is Compliant with CCPA and CPRA requirements, SAMI supports compliance with CCPA and CPRA requirements for the collection, processing, retention, and protection of personal information. Privacy-by-design controls, data minimization, access controls, and tokenization help protect personal information throughout AI-enabled workflows. Customer data processing terms are available, and organizations can support consumer rights requests, including access, deletion, correction, and data portability.
SAMI Coverage
SAMI's tokenization layer reduces exposure of personal information by replacing sensitive data before AI processing and rehydrating only authorized outputs. Continuous monitoring and audit logging provide evidence of data processing activities, access events, and remediation actions.
🇺🇸
NIST AI RMF
AI Risk Management Framework
✓ Evidence Support
SAMI maps to all four NIST AI RMF functions: Govern (policies and accountability), Map (risk identification), Measure (risk quantification via BRI/SPI), and Manage (remediation orchestration). Continuous monitoring with NIST AI RMF evidence export on demand.
SAMI Coverage
policy enforcement evidence · Map: AI system discovery · Measure: SAMI BRI/SPI scoring · Manage: remediation tracking · Evidence export for governance review.
PCI-DSS
Payment Card Industry
✓ Assessment &
Evidence Support
SAMI supports PCI-DSS Requirement 11 (penetration testing and security assessments) through continuous external and Wi-Fi security testing. Requirement 6 (application security) addressed through AI Firewall and WAF validation. Evidence packages available for QSA assessments.
SAMI Coverage
Req.6: application-security evidence support · Req. 6.4.2: public-facing web application protection evidence · Req. 11.2: wireless access point assessment support · Req. 11.3: vulnerability scanning evidence · Req. 11.4: penetration-testing documentation.
OWASP LLM Top 10
AI Security Framework
✓ Full Coverage
SAMI provides defense against all 10 OWASP LLM Top 10 categories: LLM01 Prompt Injection, LLM02 Insecure Output Handling, LLM03 RAG Poisoning, LLM04 Model DoS, LLM05 Supply Chain, LLM06 Data Disclosure, LLM07 Insecure Plugin Design, LLM08 Agent Hijacking, LLM09 Overreliance, LLM10 Model Theft. Coverage rates published quarterly.
SAMI Coverage
100% OWASP LLM Top 10 coverage across 9 security layers. Quarterly coverage reports published. OWASP IDs tagged on every SOC incident.
MITRE ATLAS
AI Adversarial Threat Landscape
✓ Aligned
SAMI maps to MITRE ATLAS the AI-specific adversarial threat framework. Unlike MITRE ATT&CK (designed for traditional infrastructure), ATLAS covers AI-native attack techniques. SAMI's attack simulation uses ATLAS technique IDs so your SOC team has consistent taxonomy.
SAMI Coverage
All current ATLAS v4 techniques mapped to SAMI defenses. Technique IDs included in every incident alert and assessment report.
EU AI Act
Regulation (EU) 2024/1689
✓ Readiness &
Evidence Support
SAMI supports EU AI Act readiness by helping organizations monitor AI systems, collect evidence, document risks, maintain transparency logs, and export structured records for internal governance and legal review. SAMI can support documentation workflows related to Article 9 risk management, Article 13 transparency, Article 17 quality management evidence, and applicable GPAI documentation needs under Articles 53–55.
SAMI Coverage
Art.9 risk documentation support · Art. 13: transparency and usage logs · Art. 17: quality-management evidence support · Art. 53–55: GPAI documentation support where applicable · Evidence export for internal review.
Zero Trust
Architecture Validation
✓ Design &
Evidence Support
SAMI applies inspection and policy enforcement at the AI model boundary for prompts, retrieval, and agent actions. It is designed to reduce implicit trust in retrieved content, user inputs, system prompts, identities, and agent actions. SAMI supports Zero Trust architecture principles described in NIST SP 800-207. SAMI does not require special network access or trust relationships to operate.
SAMI Coverage
AI interaction inspection at the model boundary · Policy enforcement for prompts, retrieval, and agent actions · Reduced implicit trust in retrieved content, user input, and system prompts · Evidence support for internal Zero Trust reviews.
Air-Gap / Zero Telemetry
Classified Environments
✓ Available
SAMI's Fully Private Zero Telemetry mode sends absolutely nothing to Autnhive. Designed for classified government, defense, nuclear, and high-security financial environments. Full platform capability all 9 security layers with zero external dependencies or data egress.
SAMI Coverage
Zero telemetry mode: fully on-premises. Air-gap deployment: supported. Classified environments: available. Three privacy modes: Fully Private, Audit, Full Retention.
Home › Legal
Legal

Legal &
Privacy Centre.

All legal documents governing your use of SAMI and Autnhive services. We publish these in full because trust requires transparency not because lawyers said we had to. For questions, requests, or DPA/BAA: info@autnhive.com

Master Use Agreement
Terms of Service
Cookie Policy
Security Policy

This Master Use Agreement ("Agreement") is entered into by and between Autnhive Corporation ("Autnhive"), and the Customer identified in the applicable Order Form ("Customer"). Each of Autnhive and Customer may be referred to individually as a "Party" and collectively as the "Parties."

1. DEFINITIONS

The following terms shall have the meanings set forth below:

  • "SAMI Platform" means Autnhive’s Secure Assessment, Testing, Simulating, Analytics, and Monitoring Intelligence platform, including all associated software, systems, configurations, threat assessment modules, business impact analysis tools, third-party vendor assessment functions, penetration testing and attack simulation capabilities, artificial intelligence engines, data ingestion and intelligence analytics features, as well as any updates, enhancements, documentation, or related technology provided by Autnhive.
  • "Customer Data" means data provided or made available by Customer, or collected from Customer's systems, networks, or infrastructure by Autnhive in the performance of the Services.
  • "Derived Data" means any data, metadata, analysis, intelligence, models, reports, conclusions, risk profiles, vulnerabilities, threat information, or liability-related assessments that are generated, inferred, or derived by the SAMI Platform or by Autnhive in the course of performing the Services, including through the processing or analysis of Customer Data or other inputs provided by or accessed from Customer.
  • "Deliverables" means any reports, analyses, risk assessments, simulation results, third-party vendor reviews, threat intelligence briefs, documentation, or other materials provided to Customer.
  • "Order Form" or "SOW" means any ordering document or statement of work that sets forth the scope of Services to be provided by Autnhive and that is executed by the Parties either in writing, electronically through digital signature, or by Customer's affirmative acceptance via an “I Agree” or “Agree to Terms and Conditions” button.
  • "Services" means all activities performed by Autnhive or its authorized subcontractors or third-party service providers under this Agreement or any applicable Order Form or SOW.
  • "Confidential Information" means any non-public, proprietary, business, technical, financial, operational, strategic, or other information disclosed by one Party to the other.

2. LICENSE AND RESTRICTIONS

License Grant. Autnhive grants Customer a limited, non-exclusive, non-transferable right to access and use the SAMI Platform and Deliverables for internal business purposes, subject to the terms of this Agreement.

Restrictions. Customer shall not:

  • (a) sublicense, resell, or distribute the Platform;
  • (b) reverse engineer, decompile, disassemble, or create derivative works;
  • (c) use the Services to build or improve a competing product;
  • (d) exceed usage limitations specified in the SOW;
  • (e) alter or obscure any proprietary notices or branding unless expressly permitted;
  • (f) use the SAMI Platform, or any data, output, or insight derived from it, in any manner intended to cause harm or adverse impact to any individual, organization, system, or third party;
  • (g) conduct or initiate any assessments, testing, or simulations on any third party without prior written approval;
  • (h) fail to implement and maintain appropriate internal safeguards, access controls, and monitoring procedures.

3. SERVICES AND ACCESS

Service Delivery. Autnhive shall provide the Services and access to the SAMI Platform using commercially reasonable efforts, skill, and diligence, and in accordance with the applicable Order Form or SOW.

Customer Responsibilities and Access. Customer shall provide Autnhive with timely access to all environments, credentials, interfaces, APIs, personnel, documentation, and resources necessary to enable Autnhive to perform the Services effectively.

Simulations and Testing. By requesting penetration tests, threat simulations, business impact analyses, or exploit emulations, Customer affirms and accepts full responsibility for initiating such assessments and confirms that it is duly authorized to allow such actions on the applicable systems.

Third-Party Authorizations. Where Testing Services are to be performed on third-party vendors or systems, Customer warrants and guarantees that it has obtained prior written authorization from a duly authorized representative of such third party.

Use and Disclosure Limitations. Customer agrees to use all information, Deliverables, or outputs resulting from Risk Assessments, Testing Services, Liability Analysis, and Compliance activities solely for internal security improvement, governance, or risk mitigation purposes.

4. DATA OWNERSHIP AND RIGHTS

Customer Data. Customer retains all right, title, and interest in and to all Customer Data. Autnhive shall access, use, or process Customer Data solely as necessary to provide the Services, fulfill its obligations under this Agreement, or as required by applicable law. Autnhive shall not disclose Customer Data to any third party without Customer’s prior written consent, except as required by law.

Autnhive may use data derived from the operation of the SAMI Platform in aggregated and anonymized form solely for internal purposes such as improving system performance, ensuring service reliability, or identifying general usage trends, provided such use does not include any identifiable or confidential Customer Data and complies with applicable data protection laws.

The following ownership and usage rights apply:

  • Derived Data. Autnhive owns all Derived Data. Customer is granted a perpetual, non-exclusive license to use such Derived Data solely for its internal governance, risk, and compliance purposes.
  • Third-Party Assessment Data. All data generated from assessments of third-party vendors belongs to Autnhive. Customer is granted a broad license to use such results for internal evaluation, compliance reporting, and audits.
  • Deletion. Upon written request and within thirty (30) days after termination, Autnhive shall delete Customer Data unless legally required to retain it. Autnhive may retain anonymized, aggregated insights.

5. SECURITY AND COMPLIANCE

Security Program. Autnhive shall maintain administrative, technical, and physical safeguards in accordance with industry best practices (e.g., ISO 27001, HIPAA, GDPR, etc.).

6. CONFIDENTIALITY

Each Party agrees to maintain the confidentiality of information disclosed under this Agreement and to use such information solely as necessary to perform its obligations or exercise its rights.

The Receiving Party shall:

  • Protect Confidential Information using at least reasonable care and no less than the care used to protect its own confidential information.
  • Disclose Confidential Information only to employees, contractors, and professional advisors with a legitimate need to know and who are bound by comparable confidentiality obligations.
  • Provide prompt written notice, where legally permissible, if compelled by law to disclose Confidential Information.

Confidential Information does not include information that:

  • (a) is or becomes publicly available through no breach of this Agreement;
  • (b) was lawfully known to the Receiving Party before disclosure;
  • (c) is received from a third party with the legal right to disclose it; or
  • (d) is independently developed without reference to the Disclosing Party’s Confidential Information.

The obligations in this Section survive for three (3) years following termination or expiration of this Agreement, except for trade secrets or information entitled to longer protection under applicable law.

7. INTELLECTUAL PROPERTY

Ownership. All right, title, and interest in and to the Autnhive Platform and related Intellectual Property shall remain exclusively owned by Autnhive, its licensors, or affiliates.

Customer shall not:

  • (a) use Autnhive Intellectual Property outside the scope of this Agreement;
  • (b) claim ownership of any Platform enhancements, modifications, or documentation;
  • (c) reproduce, distribute, modify, or disclose Autnhive Materials without prior written consent.

Additional Intellectual Property provisions:

  • Branding and White-Labeling. Any use of Autnhive trademarks, logos, or branding elements requires prior written consent and does not grant ownership rights.
  • Feedback. Autnhive may freely use, incorporate, and exploit any feedback, suggestions, or enhancement requests provided by Customer without compensation.
  • Autnhive Materials. Documents, reports, analyses, white papers, and similar materials created by Autnhive remain the sole intellectual property of Autnhive.

8. FEES AND PAYMENT

Customer shall pay all fees specified in the applicable Order Form or Statement of Work (SOW).

  • Fees. All fees are non-cancelable and non-refundable unless expressly stated otherwise.
  • Invoicing and Payment Terms. Undisputed invoices are due within thirty (30) days of the invoice date.
  • Late Payments. Overdue undisputed amounts may be subject to interest or late fees as permitted by law.
  • Taxes. Customer is responsible for all applicable taxes, duties, and charges other than taxes based on Autnhive’s net income.

9. WARRANTIES AND DISCLAIMERS

The Parties acknowledge and agree to the following warranties and disclaimers.

  • Mutual Representations. Each Party represents and warrants that it has the full power and authority to enter into and perform its obligations under this Agreement, and that doing so does not and will not violate any other agreement or legal obligation to which it is a party.
  • Limited Service Warranty. Autnhive warrants that the Services will be performed in a professional and workmanlike manner consistent with generally accepted industry standards. If Customer notifies Autnhive in writing of a material breach of this warranty within thirty (30) days of delivery of the affected Services, Autnhive will, at its sole discretion, re-perform the Services or refund the fees paid for the non-conforming portion. This remedy is Customer’s sole and exclusive remedy for breach of this warranty.
  • No Vulnerability Guarantee. Customer acknowledges that cybersecurity threat landscapes are dynamic and continuously evolving, and that the identification, detection, or simulation of vulnerabilities, threats, breaches, threat intelligence, or business intelligence is inherently probabilistic. Accordingly, Autnhive makes no representation or warranty that the SAMI Platform or Services will identify all vulnerabilities, misconfigurations, threats, or exposures in Customer’s or any third party’s environment.
  • Third-Party Services Disclaimer. The SAMI Platform may interact with, rely upon, or incorporate third-party software, services, tools, or data feeds (including threat intelligence and integrations). Autnhive does not control such third-party services and makes no warranties, express or implied, regarding their performance, availability, accuracy, security, or continued integration.
  • False Positives. While Autnhive uses commercially reasonable efforts to minimize false positives and to meet or exceed industry benchmarks in its analysis, simulations, and threat detection mechanisms, it does not guarantee the absence of false positives or false negatives. Customer is solely responsible for reviewing and validating all outputs before relying on them for operational or compliance decisions.
  • General Disclaimer. EXCEPT AS EXPRESSLY PROVIDED IN THIS SECTION, THE SAMI PLATFORM, SERVICES, DELIVERABLES, AND ANY RELATED MATERIALS ARE PROVIDED “AS IS” AND “AS AVAILABLE.” AUTNHIVE EXPRESSLY DISCLAIMS ALL OTHER WARRANTIES, EXPRESS, IMPLIED, STATUTORY, OR OTHERWISE, INCLUDING WITHOUT LIMITATION ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, NON-INFRINGEMENT, ACCURACY, OR THAT THE PLATFORM OR SERVICES WILL BE UNINTERRUPTED, ERROR-FREE, OR FREE FROM VULNERABILITIES OR SECURITY ISSUES.

10. INDEMNIFICATION

The Parties agree to the following indemnification obligations.

  • Indemnification by Autnhive. Autnhive shall defend, indemnify, and hold harmless Customer from and against any third-party claims, demands, suits, or proceedings ("Claims") to the extent arising from an allegation that the SAMI Platform, as provided by Autnhive and used by Customer in accordance with this Agreement, infringes any intellectual property rights of a third party.

Autnhive’s indemnification obligations do not apply to Claims arising from:

  • (a) Modifications to the Platform not made by Autnhive;
  • (b) Use of the Platform in combination with third-party products or services not provided or approved by Autnhive;
  • (c) Customer’s use of the Platform in violation of this Agreement or applicable law.

If the Platform becomes, or in Autnhive’s reasonable opinion is likely to become, the subject of a Claim, Autnhive may, at its option and expense:

  • (a) Procure for Customer the right to continue using the Platform;
  • (b) Replace or modify the Platform to make it non-infringing; or
  • (c) Terminate the affected Services and refund any unused, prepaid fees.
  • Indemnification by Customer. Customer shall defend, indemnify, and hold harmless Autnhive and its affiliates, officers, employees, and subcontractors from and against all third-party Claims, losses, liabilities, damages, costs, and expenses (including reasonable attorneys’ fees) arising out of or relating to:
  • (a) Customer Data or Customer’s use of the Services in violation of law or third-party rights;
  • (b) Any unauthorized security testing, simulations, or assessments initiated by Customer or performed without proper third-party consent;
  • (c) Customer’s breach of this Agreement or misuse of the SAMI Platform;
  • (d) Any failure by Customer to safeguard sensitive outputs or to restrict access by internal personnel in accordance with Section 3.
  • Conditions for Indemnification. The indemnified Party must promptly notify the indemnifying Party in writing of any Claim, provide reasonable cooperation and assistance at the indemnifying Party’s expense, and allow the indemnifying Party sole control of the defense and settlement. The indemnifying Party may not settle any Claim that imposes obligations or liability on the indemnified Party without its prior written consent (not to be unreasonably withheld).

11. LIMITATION OF LIABILITY

The Parties agree that liability under this Agreement shall be subject to the limitations set forth below.

  • Exclusion of Certain Damages. TO THE MAXIMUM EXTENT PERMITTED BY LAW, IN NO EVENT SHALL EITHER PARTY BE LIABLE TO THE OTHER FOR ANY INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, PUNITIVE, OR CONSEQUENTIAL DAMAGES, INCLUDING BUT NOT LIMITED TO LOST PROFITS, LOST REVENUE, BUSINESS INTERRUPTION, OR LOSS OF DATA, EVEN IF SUCH PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES AND REGARDLESS OF THE THEORY OF LIABILITY.
  • Cap on Liability. EXCEPT FOR LIABILITY ARISING FROM A PARTY’S GROSS NEGLIGENCE, WILLFUL MISCONDUCT, BREACH OF CONFIDENTIALITY, OR INDEMNIFICATION OBLIGATIONS UNDER THIS AGREEMENT, THE TOTAL AGGREGATE LIABILITY OF EITHER PARTY UNDER THIS AGREEMENT SHALL NOT EXCEED THE AMOUNTS PAID OR PAYABLE BY CUSTOMER TO AUTNHIVE UNDER THE APPLICABLE ORDER FORM OR SOW IN THE TWELVE (12) MONTHS IMMEDIATELY PRECEDING THE EVENT GIVING RISE TO THE CLAIM.

12. TERM AND TERMINATION

This Agreement shall remain in effect unless terminated in accordance with the provisions set forth below.

  • Term. This Agreement shall commence on the Effective Date and shall remain in effect until terminated in accordance with this Section or as otherwise set forth in an applicable Order Form.
  • Termination for Convenience. Either Party may terminate this Agreement or any Order Form upon thirty (30) days’ prior written notice to the other Party.
  • Termination for Cause. Either Party may terminate this Agreement or any Order Form immediately upon written notice if the other Party:
    • (a) Commits a material breach of this Agreement and fails to cure such breach within thirty (30) days after receiving written notice, or within an approved extension period not exceeding sixty (60) days from the original notice date, provided diligent efforts to cure continue in good faith;
    • (b) Becomes insolvent or is subject to bankruptcy, receivership, liquidation, or assignment for the benefit of creditors;
    • (c) Violates any applicable law, regulation, or governmental order, including data privacy, export control, or sanctions laws;
    • (d) Engages in conduct that could subject Autnhive to legal liability or regulatory penalties; or
    • (e) Assigns or attempts to assign this Agreement without Autnhive’s prior written consent.
  • Effect of Termination. Upon expiration or termination of this Agreement for any reason:
    • (a) All rights granted to Customer under this Agreement, including access to and use of the Platform, shall immediately terminate;
    • (b) Each Party shall promptly return or destroy the other Party’s Confidential Information in accordance with the Confidentiality provisions of this Agreement; and
    • (c) All payment obligations accrued as of the date of termination shall become immediately due and payable.
  • Early Termination Consequences for Committed Terms. If this Agreement or an applicable Order Form includes a fixed subscription term or annual purchase commitment, and such Agreement or Order Form is terminated by Customer for convenience or by Autnhive pursuant to Sections 12(c) or 12(d), Customer shall remain liable for all fees that would have become due for the remainder of the committed term. Any pricing discounts or overage waivers granted based on the commitment may be retroactively withdrawn, and Autnhive reserves the right to invoice Customer at standard rates.

13. SURVIVAL

The rights and obligations of the Parties under Sections pertaining to Confidentiality, Intellectual Property, Indemnification, Limitation of Liability, and any other provisions which by their nature should survive, shall survive termination or expiration of this Agreement.

14. TERM AND TERMINATION

  • Term. The term of this Agreement begins on the Effective Date and continues until terminated in accordance with its terms.
  • Termination Rights. Either Party may terminate this Agreement with thirty (30) days’ written notice or immediately upon a material breach by the other Party.
  • Post-Termination Obligations. Upon termination, all rights to access and use the Platform shall cease, and all Confidential Information must be returned or securely deleted.
  • Immediate Termination Events. Insolvency or assignment without required written consent shall entitle Autnhive to immediately terminate this Agreement.

15. MISCELLANEOUS

  • Governing Law. This Agreement shall be governed by and construed in accordance with the laws of the Province of Ontario, Canada, without regard to conflict of laws principles.
  • Assignment. Neither Party may assign or transfer this Agreement, in whole or in part, without the prior written consent of the other Party, except to an affiliate or in connection with a merger, acquisition, or sale of all or substantially all of the assigning Party’s assets.
  • Audit Rights. Autnhive may, no more than once per calendar year and upon ten (10) business days’ prior written notice, audit Customer’s use of the Autnhive Platform solely to verify compliance with this Agreement, including licensing scope, usage limitations, and financial obligations.
  • Audit Procedures. Any audit shall be conducted during regular business hours and in a manner that does not unreasonably disrupt Customer’s normal operations. If the audit reveals unauthorized usage, material non-compliance, or an underpayment exceeding the applicable tolerance threshold or five percent (5%) of fees owed during the audited period, Customer shall promptly pay all outstanding amounts and reimburse Autnhive for the reasonable cost of the audit.
  • Non-Solicitation. During the term of this Agreement and for twelve (12) months thereafter, Customer shall not directly or indirectly solicit or hire any employee or contractor of Autnhive who was materially involved in providing the Services without Autnhive’s prior written consent.
  • Ethical Use. Customer shall not engage in any conduct, business practice, or marketing activity that could reasonably be expected to harm Autnhive’s reputation or violate applicable laws, regulations, or recognized ethical standards.
  • Export Compliance. Each Party agrees to comply with all applicable export control laws and regulations, including those of Canada, the United States, and any other relevant jurisdictions.
  • Force Majeure. Neither Party shall be liable for any failure or delay in performance due to causes beyond its reasonable control, including acts of God, war, terrorism, labor disputes, pandemics, or governmental restrictions.
  • Independent Parties. The Parties are independent contractors. Neither Party nor their personnel shall be deemed agents, employees, partners, or joint venturers of the other Party, nor shall either Party have authority to bind the other. Autnhive shall remain solely responsible for hiring and managing personnel necessary to perform the Services.
  • Government Use. If the Autnhive Platform is licensed for use by any government agency, it is provided as “Commercial Computer Software,” and its use, duplication, or disclosure shall be subject to applicable commercial software restrictions under law.
  • Entire Agreement. This Agreement, together with all Order Forms, Schedules, and Statements of Work incorporated by reference, constitutes the entire agreement between the Parties and supersedes all prior or contemporaneous understandings, proposals, and agreements, whether written or oral, relating to its subject matter.

IN WITNESS WHEREOF, the Parties have caused this Agreement to be executed by their duly authorized representatives.

16. Contact

Data controller: Autnhive Inc., Toronto, Ontario, Canada.
Privacy enquiries: legal@autnhive.com

This Terms of Service Agreement (“Agreement”) constitutes a binding legal contract between you (“You” or “User”) and Autnhive, Inc. (“Autnhive,” “Company,” “we,” or “us”), governing your access to and use of the website located at [www.autnhive.com] and any affiliated websites or services (collectively, the “Site”).

BY ACCESSING OR USING THE SITE, YOU ACKNOWLEDGE THAT YOU HAVE READ, UNDERSTOOD, AND AGREED TO BE BOUND BY THESE TERMS AND THE PRIVACY POLICY. IF YOU DO NOT AGREE, YOU MUST NOT ACCESS OR USE THE SITE.

1. License and Restrictions on Use

Autnhive grants You a limited, non-exclusive, non-transferable, and revocable license to access and use the Site solely for lawful, personal, and non-commercial purposes, subject to these Terms. You shall not:

  • a. Reproduce, modify, distribute, or publicly display the Site or its content for commercial purposes without Autnhive’s prior written consent;
  • b. Use data mining, scraping, robots, or similar data gathering methods;
  • c. Frame or mirror any part of the Site or circumvent any security or access restrictions;
  • d. Upload or transmit malicious code, viruses, or engage in activity that interferes with the Site’s operation;
  • e. Use the Site in violation of any applicable law or regulation.

2. Use of Site Materials

All materials and content made available on or through the Site, including text, images, graphics, sound, video, software, and documentation (“Materials”), are owned by or licensed to Autnhive or its third-party providers and are protected by intellectual property laws. You shall not:

  • a. Reproduce, republish, upload, post, transmit, or distribute any Materials without Autnhive’s prior written permission;
  • b. Use Materials for commercial or derivative purposes unless expressly permitted in writing;
  • c. Rely on Materials for accuracy or fitness for any specific purpose. Materials may be inaccurate, outdated, or incomplete and are provided “AS IS.”

3. Account Responsibilities

If you create an account on the Site, you agree to:

  • a. Maintain the confidentiality of your username and password;
  • b. Accept responsibility for all activity under your account;
  • c. Notify Autnhive immediately of any unauthorized use or security breach;
  • d. Autnhive reserves the right to suspend or terminate accounts at its sole discretion.

4. Purchases and Transactions

All purchases of Autnhive’s products or services are governed by additional terms that will be presented at the time of transaction. Unless stated otherwise:

  • a. All purchases are final and provided “AS IS” without warranties;
  • b. Autnhive disclaims all liability for errors, delivery issues, pricing changes, or product availability;
  • c. You agree to be bound by any additional terms applicable to promotions, discounts, or product offerings.

5. Third-Party Links and Content

The Site may include links to third-party websites for your convenience. Autnhive does not control, endorse, or assume any responsibility for third-party content, availability, or actions. Use of such websites is at your own risk. You are solely responsible for taking appropriate precautions to protect yourself from malware, phishing, or other harmful content.

6. Electronic Communications

By using the Site or communicating electronically with Autnhive, you consent to receive communications electronically, including notices, agreements, disclosures, and other communications that satisfy legal requirements for written communication.

7. Government Use

Any software or Materials provided through the Site for Government use are provided with restricted rights. Use, duplication, or disclosure is subject to applicable regulations under DFARS 227.7202-3 or FAR 52.227-19.

8. User Submissions

Unless otherwise agreed in writing, any comments, feedback, data, or other submissions provided by you to Autnhive (“Submissions”) shall be considered non-confidential and become the property of Autnhive. You agree to:

  • a. Assign to Autnhive all rights, title, and interest in any Submissions;
  • b. Warrant that Submissions are lawful and do not infringe any third-party rights;
  • c. Authorize Autnhive to use, display, modify, and distribute Submissions without restriction or compensation.

9. Disclaimer of Warranties

THE SITE AND ALL MATERIALS ARE PROVIDED ON AN “AS IS” AND “AS AVAILABLE” BASIS. AUTNHIVE EXPRESSLY DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, NON-INFRINGEMENT, AND ACCURACY. AUTNHIVE DOES NOT WARRANT THAT:

  • a. THE SITE OR MATERIALS WILL BE ERROR-FREE OR UNINTERRUPTED;
  • b. DEFECTS WILL BE CORRECTED;
  • c. ANY CONTENT IS ACCURATE, CURRENT, OR RELIABLE;
  • d. SOME JURISDICTIONS MAY NOT ALLOW THE EXCLUSION OF CERTAIN WARRANTIES. IN SUCH CASES, AUTNHIVE’S LIABILITY IS LIMITED TO THE MAXIMUM EXTENT PERMITTED BY LAW.

10. Limitation of Liability

TO THE MAXIMUM EXTENT PERMITTED BY LAW, IN NO EVENT SHALL AUTNHIVE BE LIABLE FOR ANY INDIRECT, INCIDENTAL, CONSEQUENTIAL, SPECIAL, EXEMPLARY, OR PUNITIVE DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO DAMAGES FOR LOST PROFITS, LOSS OF DATA, BUSINESS INTERRUPTION, OR OTHER ECONOMIC LOSS, ARISING OUT OF OR IN CONNECTION WITH YOUR USE OF THE SITE OR RELIANCE ON MATERIALS, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. YOUR SOLE AND EXCLUSIVE REMEDY IS TO DISCONTINUE USING THE SITE.

11. Indemnification

You agree to indemnify, defend, and hold harmless Autnhive, its affiliates, officers, directors, employees, agents, licensors, service providers, successors, and assigns from and against all claims, damages, liabilities, losses, costs, and expenses (including attorneys’ fees) arising from or related to:

  • a. Your access to or use of the Site;
  • b. Your reliance on any Site content;
  • c. Your violation of these Terms or any applicable law;
  • d. Any Submission or content you provide;
  • e. Your infringement of any intellectual property, privacy, or other rights of a third party;
  • f. This indemnification obligation survives termination of this Agreement.

12. Force Majeure

Autnhive shall not be held liable for any failure or delay in performance caused by events beyond its reasonable control, including but not limited to acts of God, war, cyberattacks, labor disputes, supply chain disruption, pandemics, or internet outages.

13. Arbitration; Waiver of Class Actions

All disputes arising out of or relating to these Terms shall be resolved through binding arbitration in accordance with the rules of the Canadian Arbitration Association. You agree to resolve disputes on an individual basis and waive any right to participate in class actions or jury trials. Venue for any arbitration shall be Oakville, Ontario.

14. Export Compliance

You agree to comply fully with all U.S. and Canadian export control laws and regulations. You shall not export or re-export any Materials or software obtained from the Site to any country or entity prohibited by applicable law, including embargoed nations or sanctioned individuals.

15. Modifications to Terms

Autnhive reserves the right to modify these Terms at any time by posting updated terms on the Site. Your continued use of the Site after such changes constitutes your acceptance of the revised Terms.

16. Governing Law and Jurisdiction

These Terms shall be governed by and construed in accordance with the laws of the Province of Ontario and applicable federal laws of Canada, without regard to conflict of laws principles. You consent to the exclusive jurisdiction and venue of Courts located in Halton Region, Ontario.

17. Entire Agreement; Severability

These Terms constitute the entire agreement between you and Autnhive concerning the subject matter herein. If any provision is found unenforceable, the remaining provisions shall remain in full force and effect. Failure to enforce any provision shall not constitute a waiver of that right.

18. Intellectual Property Notices

The Site and all Materials are protected by copyright, trademark, and other intellectual property laws. Autnhive’s name, logos, and related marks are trademarks of Autnhive, Inc. You may not use such marks without prior written permission. Other product and company names may be trademarks of their respective owners.

19. Copyright Infringement (DMCA Notice)

If you believe your work has been used in a way that constitutes copyright infringement, please contact our Copyright Agent with the following:

  • a. Your physical or electronic signature;
  • b. Identification of the copyrighted work;
  • c. Description of the infringing material;
  • d. Your contact details;
  • e. A statement of good faith belief the use is unauthorized;
  • f. A statement made under penalty of perjury of accuracy and authority.

20. Contact

For questions regarding these Terms or to request permissions, contact: legal@autnhive.com

Data Processing Agreement (DPA) Standard Terms. This is our standard DPA for GDPR and PIPEDA compliance. A signed, countersigned DPA is available on request. Contact: info@autnhive.com

1. Scope and Roles

This DPA applies where Autnhive processes personal data on your behalf as part of the SAMI service. You are the data controller; Autnhive is the data processor. Where Autnhive processes data for its own purposes (e.g., account management), Autnhive is an independent controller.

2. Subject Matter

Autnhive processes personal data only as necessary to provide the SAMI service: security assessment findings, remediation tracking, compliance evidence generation. The categories of data subjects are your employees, contractors, and system users whose activity is monitored by SAMI.

3. Processing Instructions

Autnhive processes personal data only on documented instructions from you. The service agreement constitutes your initial instructions. You may provide further written instructions at any time. Autnhive will notify you if any instruction appears to violate applicable data protection law.

4. Confidentiality

Autnhive personnel with access to personal data are bound by confidentiality obligations. Access is restricted to personnel whose role requires it. Background checks and security training required for all personnel with data access.

5. Sub-processors

Autnhive's current sub-processors: Oracle Cloud Infrastructure (Canada/EU hosting), SendGrid (email delivery). We provide 30 days advance notice of sub-processor changes. You may object to new sub-processors within 14 days. All sub-processors are bound by equivalent data protection obligations.

6. Security Measures

Autnhive implements Article 32 GDPR technical and organizational measures: encryption in transit (TLS 1.3) and at rest (AES-256), access controls and MFA, regular penetration testing, ISO 27001 and SOC 2 Type II certification, incident response procedures with 72-hour breach notification capability.

7. Data Subject Rights

Autnhive will assist you in fulfilling data subject rights requests within 5 business days of your request. Autnhive will not respond directly to data subjects without your authorisation except where legally required.

8. Data Breach Notification

Autnhive will notify you of a personal data breach within 48 hours of becoming aware. Notification will include the nature of the breach, categories and approximate number of individuals affected, likely consequences, and measures taken or proposed. This supports your 72-hour GDPR notification obligation.

9. Data Transfers

Data is processed in Canada (adequacy decision) and EU-based Oracle Cloud regions. No transfers to countries without adequate protection without appropriate safeguards (SCCs or equivalent).

10. Return and Deletion

On termination of the service, Autnhive will provide a data export within 30 days and delete all personal data within 60 days of export confirmation, unless legal retention obligations apply. Zero Telemetry deployments have no Autnhive-side data to return.

11. Audits

You may request audit evidence (SOC 2 report, ISO 27001 certificate, penetration test summary) once per year. On-site audits available with 30 days notice and reasonable cost reimbursement.

12. BAA (HIPAA)

A HIPAA Business Associate Agreement is available for covered entities. The BAA covers SAMI's processing of protected health information (PHI) in clinical AI deployments. SAMI's tokenization architecture processes tokens, not PHI but the BAA covers the full service scope regardless.

To request a signed DPA or BAA: info@autnhive.com

Last updated: April 11, 2026. This Cookie Policy explains how Autnhive uses cookies and similar tracking technologies on autnhive.com. You can manage cookie preferences at any time using your browser settings.

1. What Are Cookies

Cookies are small text files stored in your browser when you visit a website. They allow the site to recognise your device and remember preferences. We also use local storage and session storage for functional purposes.

2. Cookies We Use

Essential Cookies (always active)

  • Session management: maintain your login state and preferences during a session. Deleted on browser close.
  • Security: CSRF protection tokens, rate-limiting identifiers. Required for the service to function securely.
  • Cookie consent: remember your cookie preference choices.

Analytics Cookies (consent required)

  • Usage analytics: anonymised page views, time on page, navigation paths to understand how the site is used and improve it. No personal identifiers stored.
  • We use server-side analytics that do not set third-party cookies where possible.

What We Do NOT Use

  • No advertising or retargeting cookies
  • No social media tracking pixels
  • No cross-site tracking
  • No fingerprinting or behavioural profiling

3. Managing Cookies

You can block or delete cookies through your browser settings. Essential cookies cannot be disabled without affecting core site functionality. For analytics cookies, use your browser's "Do Not Track" setting or contact us to opt out.

4. Contact

Cookie or tracking enquiries: info@autnhive.com

Last updated: April 11, 2026. Autnhive's security programme is built to the same standard we hold our customers to. We publish this policy so you can independently assess our security posture before trusting us with your environment.

1. Security Certifications

  • ISO 27001:2022 certified
  • SOC 2 Type II attested
  • GDPR compliant
  • HIPPA compliant
  • CCPA compliant

2. Infrastructure Security

  • Cloud: Oracle Cloud Infrastructure, certified data centres (Canada, EU)
  • Encryption: TLS 1.3 in transit, AES-256 at rest, key management via OCI Vault
  • Network: WAF, DDoS protection, private networking between components
  • Access: MFA required for all Autnhive personnel, just-in-time access provisioning

3. Application Security

  • Security-by-design development practices, OWASP ASVS Level 2 compliance target
  • Automated SAST/DAST in CI/CD pipeline for every code change
  • Annual external penetration test by independent firm
  • Dependency scanning and SBOM maintained for all production software

4. Vulnerability Disclosure

We welcome responsible disclosure of security vulnerabilities. Please report to info@autnhive.com with "Vulnerability Disclosure" in the subject. We commit to: acknowledge within 24 hours, provide a resolution timeline within 5 business days, not pursue legal action against good-faith researchers.

5. Incident Response

  • 24/7 security monitoring via SAMI (we use our own platform)
  • Incident response team on-call, CSIRT procedures in place

6. Personnel Security

  • Background checks for all personnel with system access
  • Security awareness training quarterly
  • Least-privilege access model, access reviewed quarterly

7. Penetration Test Summary

Annual penetration test conducted by independent security firm. Most recent: Q1 2026. Scope: full platform including AI components, cloud infrastructure, and APIs. All critical and high findings remediated before publication. Executive summary available to customers under NDA.

8. Contact

Security enquiries and vulnerability reports: info@autnhive.com

Home › AI Security › AI Firewall
L01 · AI Firewall · Live Security

There is no AI incident type
in your SIEM today.
SAMI creates the first one.

The only AI security platform that inspects all four directions simultaneously inbound prompts, outbound responses, RAG retrieval, and every MCP/agent tool call feeding first-class incidents into your existing SIEM/SOAR. No new workflows. No retraining.

The board question
"Can your SOC see when your AI is being attacked right now in real time? Not after an alert. Right now."
Why no other tool catches this

AI attacks look like normal traffic.
To every tool you currently have.

A prompt injection doesn't exploit a vulnerability. It manipulates language. It passes all authentication checks. To your Splunk, Sentinel, or QRadar instance, a poisoned AI response is byte-for-byte identical to a legitimate one. There is nothing for traditional security tools to detect. SAMI reads the semantic layer they were never designed to see.

✓
4-direction coverage ★ UNIQUE inbound prompts, outbound responses, RAG retrieval queries + retrieved content, and MCP/agent tool calls. Lakera, Protect AI, Azure AI Content Safety, and AWS Bedrock cover important parts of prompt/response guardrails. SAMI unifies prompt, response, retrieval, and MCP/agent control in one workflow.
✓
SOC first-class AI incidents ★ UNIQUE structured events with request IDs, reason codes, component-level decisions fed directly into your existing SIEM/SOAR queue. No new workflow. No retraining. AI attacks finally visible to your SOC.
✓
Quarantine mode ★ UNIQUE ambiguous threats held in a SOC analyst queue for human review: allow, redact, block, or escalate. Every other platform forces binary block/allow. Grey area gets a human.
✓
Tokenize-before-AI ★ UNIQUE PII and proprietary data tokenized locally before any model contact. SAMI rehydrates real values in output. Model never saw the data. Supports GDPR Article 25 privacy-by-design by reducing model exposure to identifiable data before AI processing.
✓
Autonomous Response Engine integration when BLOCK fires, all 9 layers are notified simultaneously. TrustGuard tightens. RAG Defender updates. Agentic Security restricts. All in 3.9 seconds. Without a human.
Competitors cover parts of AI safety, governance, model risk, or guardrail enforcement. SAMI closes the loop: four-surface control, quarantine, AI-specific incidents, CTEM, and financial exposure scoring.
Five enforcement decisions hard outcomes, not probability scores
✅
ALLOW
Clean interaction. Full audit log, request ID, and EU AI Act Art.9 compliance evidence auto-generated for every single interaction.
★ UNIQUE 🔍
QUARANTINE
Suspicious but ambiguous. Held in SOC analyst queue. Human decides: allow, redact, block, or escalate. SAMI keeps grey-area AI risk human-reviewed with quarantine before trust.
✂️
REDACT
PII or sensitive data stripped from prompt or response. Sanitized version delivered. User experience uninterrupted. Productivity preserved.
★ UNIQUE 🔑
TOKENIZE
Sensitive data replaced with tokens before model contact. SAMI rehydrates real values in output. The AI model never saw PII or proprietary data. Full output accuracy maintained.
🚫
BLOCK
Malicious. Stopped immediately. SOC alerted. Autonomous Response Engine notifies all 9 layers simultaneously. Full audit trail generated with request ID and reason codes.
Calibrated Deployment™: The Firewall starts in Observe mode watching your real traffic patterns, no enforcement, zero risk. It tests itself against your actual environment, builds a Confidence Index, and only promotes to enforcement when it's ready. You never cold-start into production.
See the AI Firewall block a live attack in your environment.
⚡ Run Your AI Security Assessment
Home › AI Security › TrustGuard
L02 · TrustGuard · Guardrail Module · Policy-as-Runtime Engine

92% of organizations with guardrails
have already leaked data.
They had guardrails. They failed silently.

TrustGuard is not a guardrail. It is a policy-as-runtime engine. Hard allow/block/transform decisions on every prompt, response, and tool call not probability scores. And when bypass is detected, it autonomously tightens and fires a SOC alert simultaneously, in real time.

The CISO question no one wants to answer
"When AI data guidelines fail - and 92% of users with employer AI data guidelines still report leaking company data - runtime control matters."
Why every guardrail you have will eventually fail

Guardrails are probabilistic.
They influence probability.
They don't enforce limits.

A guardrail is a trained constraint. It shifts the probability distribution of a model's output toward refusing harmful requests. It does not enforce a hard logical boundary. Under sufficient pressure (and attackers are very good at applying that pressure) every guardrail can be circumvented. TrustGuard replaces probability with determinism.

✓
Policy-as-runtime engine ★ UNIQUE hard allow/block/transform. Not a score. Not a flag. A decision. Auditable. Traceable. Enforceable by design.
✓
Autonomous tightening + simultaneous SOC alert ★ UNIQUE when bypass detected, policy tightens AND SOC alert fires at the same moment. Not sequentially. Simultaneously. NeMo, LlamaGuard, Azure: none can do this.
✓
Session-level pattern analysis ★ UNIQUE multi-step erosion detected across the full conversation. 20 benign messages that collectively cross the boundary: caught before breach. Static guardrails evaluate per-message and miss this entirely.
✓
Policy bundle lifecycle ★ UNIQUE draft→validated→approved→published. Model updates and policy updates are fully decoupled. Ship new model versions without touching guardrail code.
✓
Multi-tenant policy isolation each team or customer runs isolated policy bundles on shared infrastructure. No code forks. No policy bleed between tenants.
✓
Dual approval with separation of duties high-risk overrides require a second approver. Self-approval blocked by policy. Exception governance built into the product, not bolted on.
✓
Audit-grade decision telemetry every runtime decision logged with request ID, component-level decisions, reason metadata, and tenant context. Mean-time-to-investigate: minutes, not hours.
Classifiers, filters, guardrails, governance, and model-risk tools are controls. SAMI runs the closed loop: detect, quarantine, escalate, tighten, document, and quantify. Multi-step erosion requires memory across the interaction. SAMI tracks attack state across turns and escalates when risk accumulates. None auto-tighten when bypassed. None fire a simultaneous SOC alert. None have policy bundle lifecycle. None have session-level analysis. SAMI maintains persistent attack memory and changes runtime controls when behavior shifts.
Six bypass techniques TrustGuard stops all in real time
Reframing "for a novel I'm writing..."
Re-contextualises as fictional. Shifts probability past the guardrail. TrustGuard evaluates intent, not framing.
Role-play injection "You are DAN, no restrictions..."
Assigns new persona with override instructions. Persona injection detected and blocked pre-execution.
Multi-step erosion 20 compliant messages, then...
Each step looks fine. Aggregate crosses the boundary. Session analysis catches it before breach.
Token smuggling "W.r.i.t.e t.h.e..."
Obfuscated encoding bypasses pattern matching. TrustGuard normalizes before evaluation. Decoded intent blocked.
Jailbreak templates DAN, AIM, STAN, JAILBREAK variants
Hundreds of public templates. New ones daily. Continuous library. Novel variants flagged and quarantined.
Context window flooding 50,000 tokens of noise
System prompt pushed to edge of context, loses influence. System prompt integrity monitoring detects and blocks.
What happens when TrustGuard detects bypass at 3am: Policy bundle tightens. SOC alert fires. Autonomous Response Engine notifies all 9 layers simultaneously. The guardrail is stronger before the attacker can try again. No human required. No delay.
Drift Detection & Gap Analysis · CTEM for AI · Continuous

The guardrails you calibrated for v1
may be wrong for v2.

Models drift. Behaviour changes between versions, fine-tunes, and deployment environments. A guardrail that worked perfectly on your original model may be over-permissive or over-restrictive on the new one and you won't know until users find the gap. SAMI detects drift and flags it before they do.

Why model updates break security controls silently

Security calibrated for the old model.
Running on the new one.

When your team deploys a new model version, fine-tune, or RLHF update, the model's behaviour distribution shifts. Guardrails calibrated for the old distribution may now be too aggressive (blocking legitimate use cases) or too permissive (missing attack vectors the new model is more vulnerable to). Without continuous drift detection, you find out from user complaints or, worse, from an incident.

✓
Behavioural drift detection ★ UNIQUE SAMI continuously monitors model output distributions and flags significant shifts. New model version detected drift analysis runs automatically before the new version receives production traffic.
✓
Compliance gap analysis identifies regulatory obligations your current controls don't cover. New compliance requirements detected affected layers notified. No manual gap assessment required.
✓
Continuous not periodic drift monitoring runs continuously at runtime, not as a scheduled scan. Real-time gap visibility. No surprises at the next audit.
✓
Feeds directly into the Autonomous Response Engine when drift is detected that creates a compliance or security gap, the ARE automatically updates the affected layers. TrustGuard policy recalibrated. Risk Assessment scores updated.
✓
Shadow model detection new AI models deployed anywhere in the organization are detected, added to the AI-BOM, and drift-monitored from first use.
The question to ask before every model update
"When you update your AI model do your security controls, guardrails, and compliance posture update with it automatically?"
What triggers drift detection and what happens next
🔄 Model version update
New model version or fine-tune deployed. SAMI detects the deployment, runs behaviour comparison against baseline, flags drift above threshold.
→ ARE recalibrates TrustGuard policy bundle for new distribution
📋 New regulatory requirement
EU AI Act implementing act, NIST guidance update, or ISO 42001 revision detected. Gap analysis runs against all AI systems in inventory.
→ ARE updates Compliance Automation monitoring scope
🌐 New attack technique published
Novel jailbreak template, MITRE ATLAS technique, or OWASP guidance update. Existing controls evaluated against new technique.
→ ARE updates Risk Assessment simulation library and reruns
🔍 Shadow AI discovered
New AI model deployment detected across the organization. Added to AI-BOM. Compliance posture assessed. Risk scored.
→ ARE adds to monitoring scope, notifies GRC of new system
No static security tool can do this. Drift detection only works if the monitoring system is continuous, connected to the live security layers, and capable of automatically updating those layers when drift is detected. That requires the Autonomous Response Engine which only SAMI has.
Guardrail Gap Assessment

Don't know what guardrails you're missing?
SAMI finds them, recommends them, and implements them.

Most teams write guardrails once, at deployment, then never update them. SAMI runs a continuous gap assessment against your live AI stack, and closes the gaps automatically.

1
Audit current guardrails

SAMI scans your current guardrail configuration against your AI stack, attack surface, and threat profile. Every gap identified, including guardrails you never knew you needed.

2
Recommend what's missing

A specific, prioritized list of recommended guardrails with explanation of what attack each one closes and what the financial exposure is without it. No generic checklists.

3
Auto-implement on approval

Your team reviews and approves. SAMI implements the guardrails automatically, no developer work, no model redeployment, no downtime. Policy-as-runtime updates instantly.

4
Self-tighten continuously

Guardrails update based on live attack patterns, new threat intelligence, and policy changes. When a bypass is detected, SAMI auto-tightens and fires a SOC alert simultaneously, no quarterly reviews required.

WHY THIS MATTERS

Guidelines are not runtime control: 92% of GenAI users with employer AI data guidelines still said they leaked company data. Your AI stack has changed since you wrote them. New attack techniques have been published. New models have been deployed. Your guardrails don't know any of this.

LlamaGuard is a classifier. NeMo is a programmable guardrail framework. SAMI is the session-aware control room around them. Classifiers and guardrails evaluate supplied content or configured policies. SAMI adds live gap detection, runtime tightening, and SOC escalation when behavior shifts. SAMI's gap assessment runs continuously, finds what your current guardrails don't cover, recommends what's missing, and implements it automatically on approval. The question isn't whether you have guardrails. It's whether they're still protecting you.

Test your guardrails against all 6 bypass techniques. Free assessment.
⚡ Run Your AI Security Assessment
Home › RAG Defender
L03 · RAG Defender · Live Security · OWASP LLM02/03

You connected your AI to your knowledge base.
That knowledge base is now an attack surface.

RAG Defender operates at two independent points: ingestion-time (before any document enters your knowledge base) and query-time (before any retrieved content reaches the model). Two layers covering 8 poisoning vectors no other platform addresses.

The question for your AI architect
"If an attacker contributed a document to your knowledge base today how many AI responses would be influenced before anyone noticed?"
The Slack AI incident and how it applies to your organization

No hacking required.
Just a message your RAG pipeline trusted.

In August 2024, an attacker with access to any public Slack channel embedded a hidden instruction in a message. When a victim later asked Slack AI to summarise their private channels, the AI executed the attacker's instruction and exfiltrated private data. No malware. No credentials. No vulnerability exploit. Just text that the RAG pipeline retrieved and trusted.

If your AI retrieves from any source that external parties can contribute to shared wikis, connected email, uploaded documents, indexed websites you have this attack surface right now.

✓
Ingestion-time quarantine workflow ★ UNIQUE suspicious documents quarantined before entering the knowledge base. Structured approve/reject review loop with audit events. No competitor has this operational workflow. Tainted content cannot accumulate silently.
✓
Query-time indirect injection removal ★ UNIQUE retrieved context re-screened before model contact. Hidden instructions detected and stripped. The Slack AI attack stopped here.
✓
8 poisoning vectors covered including embedding manipulation and supply-chain attacks that content-level filters cannot detect.
✓
Multi-tenant knowledge base isolation one tenant's documents cannot influence another tenant's AI responses. Cross-tenant data leakage structurally prevented.
✓
Agentic pipeline support RAG Defender covers agentic retrieval workflows. Poisoned context cannot propagate through multi-step agent chains.
Lakera Guard covers some inbound injection detection. Protect AI Guardian covers model/package risk. RAG needs two checkpoints: before storage and before generation.SAMI inspects both. None have ingestion-time quarantine workflow. None cover embedding poisoning or supply-chain attacks.
Some ways attackers poison what your AI retrieves and believes
Document injection
Hidden instructions in shared documents hijack AI responses when retrieved.
Web content poisoning
Malicious indexed pages inject false authoritative instructions via RAG.
Database manipulation
Tampered records cause AI to present false data as verified fact.
Supply chain poisoning
Third-party data feeds compromised before ingestion. The SolarWinds pattern applied to AI knowledge.
Embedding poisoning
Vector embeddings biased to retrieve malicious content. Invisible to all content-level filters.
Context collision
Injected content overrides legitimate context in the retrieval window.
Chunk-level injection
Instructions buried inside document chunks to survive RAG splitting.
Ranking manipulation
Attacker content crafted to score highest in retrieval and be prioritized by the model.
Two-layer defense both in the execution path: Ingestion screening catches the document before it enters the KB. Query-time screening catches the hidden instruction before the model sees it. An attacker who bypasses one still faces the other.
Test your RAG pipeline against all 8 poisoning vectors.
Free assessment. No code changes.
Home › AI Security › Agentic Security
L04 · Agentic Security · Live Security · OWASP LLM08 · MCP

Your agent has a database connection.
Who validated what it can do with it?

AI agents are gaining tool access faster than governance is maturing. When agents have MCP tool access file systems, databases, APIs, email a single injected instruction executes real-world actions that look identical to normal AI usage. Traditional tools may log the call. SAMI decides whether the agent action should be allowed.

The security question no one is asking
"If an AI agent took an unsafe autonomous action right now would anyone in your security team know before the damage was done?"
Why MCP transforms a language vulnerability into a real-world action

The attacker doesn't touch your infrastructure.
The agent does it for them.

An injected instruction that without tool access would produce harmful text, with MCP tool access queries your customer database, sends emails on behalf of an executive, modifies records, and calls external APIs all inside a legitimate, authenticated, approved workflow. Your firewall logs show normal traffic. Your SIEM shows no anomaly. The action has already been taken.

✓
MCP tool call validation ★ UNIQUE every tool call validated against approved policy scope before execution. Unknown tools blocked. Scope violations caught before action, not logged after.
✓
Dual approval with separation of duties high-privilege tools require a second approver. Self-approval blocked by policy. The executive whose email the agent wants to send cannot approve their own exception.
✓
Agent memory governance cross-session memory access monitored. Memory poisoning attempts detected. Long-term context manipulation across agent sessions prevented.
✓
Inter-agent chain security in multi-agent orchestration, poisoned instructions cannot propagate from one agent to another via message passing. Chain attacks stopped at every boundary.
✓
Continuous LLM + MCP risk assessment new model versions and new tool connections assessed automatically before first use. Not point-in-time. Continuous coverage as your agent stack evolves.
✓
Full session audit trail every tool attempt, every decision, every approval logged with correlation IDs. If an incident investigation needs to reconstruct what an agent did and why, the evidence is already there.
No competing platform validates MCP tool calls at the policy level before execution. Prompt screening is not enough for agents. SAMI checks MCP and agent actions before execution. NeMo Guardrails has no agentic tool governance. SAMI enforces MCP and agent actions before execution with scope checks, approval logic, quarantine, and liability scoring. SAMI is the only platform where every agent action goes through a scope policy gate before it executes.
What happens when an agent attempts a scope violation
1
Agent initiates workflow
Scope, memory, and approved tool access validated against active policy.
→
2
Tool call attempted outside approved scope
Agent tries to invoke file_write not in approved policy. Intercepted before execution.
⚠
3
Scope violation blocked SOC alerted
Tool call blocked pre-execution. File system never touched. SOC incident raised immediately.
✗
4
Dual approval triggered
If legitimately needed, a second approver is required. Self-approval blocked. Separation of duties enforced.
→
5
Full session audit trail complete
Every tool attempt, decision, and approval logged. Incident investigation: minutes.
✓
The key distinction: This happens before the action executes. Not after. The database is never queried. The email is never sent. The file is never written. Pre-execution governance is the only governance that matters for agentic AI.
Assess your agentic AI attack surface. Free assessment.
Home › Data Privacy & Tokenization

AI productivity should not require data exposure.
Your teams want to use AI to move faster.

The problem is that most AI tools make the boundary between “useful assistance” and “sensitive data exposure” invisible. Every prompt can carry proprietary data, personal information, health data, customer records, contracts, credentials, or regulated context into systems you do not fully control. SAMI makes privacy structural, so data protection does not slow down your AI journey. Sensitive data is detected, redacted, tokenized, blocked, or processed locally before it reaches the model.

YOUR DPO NEEDS PROOF, NOT PROMISES
"When employees or your customers use AI with proprietary, personal, or regulated data, SAMI proves what happened: what was allowed, what was redacted, what was tokenized, what was blocked, and what stayed inside your environment, which privacy data did your AI access or which was sent out etc.
Why filtering output is not privacy and why architecture is

Most AI security tools create
the exposure they claim to prevent.

To inspect a prompt for sensitive data, most AI security tools send the prompt to their cloud for analysis. That means your engineers' proprietary code, your customers' PII, your confidential context all transmitted to a third party for processing. SAMI's Fully Private mode sends nothing. All analysis runs locally. Autnhive sees no data. Ever.

Tokenize-before-AI goes further. Sensitive data is tokenized locally before any model contact. The AI model whether local or external receives only tokenized content and processes it without ever seeing the sensitive values. SAMI rehydrates the real values in the output. Full accuracy. Zero exposure.

✓
Tokenize-before-AI ★ UNIQUE PII, PHI, and proprietary data tokenized locally before model contact. Model processes tokens only. SAMI rehydrates real values in output. Model never saw the data.
✓
GDPR Art.25 by architecture not detection, not filtering-after-the-fact. Structural privacy. The model cannot access what was never in its input.
✓
Calibratable privacy 3 modes ★ UNIQUE Fully Private (zero telemetry), Audit Mode (metadata only), Full Retention (complete forensic trail). Full SAMI capability across all three modes.
✓
Air-gapped from day one designed for classified defense and high-security banking as the default deployment, not a premium add-on. Zero external calls under any circumstance in Fully Private mode.
✓
On-prem sidecar deploys alongside your AI infrastructure. Compatible with Llama, Mistral, Falcon, and any fine-tuned model. No architecture changes required.
No other AI security platform offers a zero-telemetry privacy mode with full capability. SAMI supports private, on-prem, air-gapped, and zero-telemetry deployment where required. IBM watsonx.governance: partial on-prem only. SAMI is the only platform where full capability and zero data exposure are the same deployment mode.
How tokenization works step by step
1
Input arrives with sensitive data
PII, PHI, trade secrets, proprietary code detected by SAMI input screening locally.
⚠
2
Tokenization applied locally
Sensitive identifiers tokenized. Nothing leaves the environment.
→
3
Model processes tokens only
AI model local or external receives only tokenized content. Full analytical capability maintained.
→
4
SAMI rehydrates real values
SAMI rehydrates local PI data and user receives complete, accurate response.
✓
Sensitive data never left the jurisdiction
Engineer productive. IP protected. No ban required. GDPR Art.25 satisfied by design.
✓
Fully Private Zero Telemetry
All analysis local. Autnhive sees no data. Air-gapped and classified environments. Full SAMI capability.
Audit Mode Metadata Only
Event types and timestamps only. No content transmitted. GDPR-sensitive deployments. EU AI Act evidence trail.
Full Retention Complete Forensic Trail
Full interaction logging. SOC 2 Type II. Financial regulators. Legal hold. Every decision traceable.
See how SAMI protects sensitive data in your AI workflows.
Free assessment. No code changes.
Home › Risk Assessment & Simulation

An AI vulnerability assessment before launch
cannot protect you from attack techniques that emerge after launch.

AI attack methods change constantly: prompt injection, jailbreak variants, model manipulation, agent hijacking, tool-call abuse, RAG poisoning, sensitive-data extraction and more. Even AI red teaming performed before launch becomes stale as new attack paths appear. SAMI continuously conducts vulnerability assessment and simulates adversarial attacks across the LLM, MCP and Agents, external facing AI system, so your defenses keep up as attackers change.

The CTO question
"Have you tested your AI against jailbreak and prompt injection attacks and is that testing continuous, or was it a one-time engagement six months ago?"
Why continuous simulation is the only answer

Attackers don't wait for
your next scheduled assessment.

The pen tester who assessed your AI in January used techniques that existed in January. The attacker targeting you in October has access to techniques published in September, August, and July plus zero-days the community hasn't documented yet. Continuous simulation means SAMI always tests with the latest known techniques, not the ones from your last engagement.

✓
Three attack surfaces LLM, MCP, Agentic ★ UNIQUE continuous adversarial simulation across LLM endpoints, agentic pipelines, and MCP tool connections simultaneously. No competitor covers all three.
✓
OWASP LLM Top 10 v1.1 full coverage of all 10 AI attack vectors, continuously. Not a checkbox compliance exercise.
✓
MITRE ATLAS the AI-specific adversarial attack framework. Full technique library, continuously applied.
✓
BRI per finding ★ UNIQUE every finding assigned a dollar-value breach exposure score. Fix what reduces liability most. Not fix what has the highest CVSS label. Fix what costs you most if exploited.
✓
SEC material risk ready BRI financial liability per AI system using IBM Cost of Breach methodology. P50/P75/P95 scenarios. Board-ready language. SEC material risk disclosure-ready.
SAMI combines : AI attack simulation with FLE/SPI/BRI impact scoring. NeMo Guardrails partial LLM testing only, no MCP or agentic coverage. IBM watsonx.governance no attack simulation. SAMI is the only platform offering continuous simulation across all three AI surfaces with financial liability scoring per finding.
OWASP LLM Top 10 full coverage, all environments
LLM01
Prompt Injection direct and indirect
CRITICAL
LLM02
Insecure Output Handling
CRITICAL
LLM03
Training Data Poisoning / RAG Poisoning
HIGH
LLM06
Sensitive Information Disclosure
CRITICAL
LLM08
Excessive Agency agent scope violation
CRITICAL
+5 more
LLM04 DoS · LLM05 Supply Chain · LLM07 Plugins · LLM09 Overreliance · LLM10 Theft
COVERED
The BRI difference: Every finding gets a dollar value. "Critical severity" is a label. "$2.4M expected breach cost if exploited" is a number your CFO understands, your board approves remediation for, and your SEC filing can reference. SAMI speaks the language that gets things fixed.
See what attackers would find in your AI environment.
Free assessment. No code changes.
Home › AI Security › AI Exposure & AI-BOM
L07 · AI Exposure & AI-BOM · CTEM for AI · Shadow AI · Supply Chain

You can't secure
what you don't know exists.

The average enterprise has 3× more AI models deployed than IT knows about. Shadow AI models deployed by teams without IT visibility carries the same attack surface as approved AI, with zero governance. Your AI-BOM is the foundation of everything. Without it, you are governing a fraction of your actual exposure.

The inventory problem no tool was solving

Every model your teams deployed
without telling IT is a live attack surface.

Engineers deploy local Llama instances. Product teams connect to third-party LLM APIs. Data scientists run fine-tuned models on internal GPU. Marketing connects to AI writing tools. None of these went through security review. All of them have the same attack surface as your approved AI systems. SAMI surfaces every one of them.

✓
Full AI-BOM including shadow AI ★ UNIQUE complete inventory of every model, agent, pipeline, and tool connection across the organization. Shadow AI surfaced automatically. You cannot govern what you cannot see.
✓
Model provenance and fine-tuning history where every model came from, what data it was trained on, what fine-tuning has been applied. Supply chain risk mapped per model.
✓
BRI financial liability per AI system ★ UNIQUE dollar-value breach exposure quantified for every model in the inventory. SEC material risk disclosure-ready. The board can see exactly which AI systems carry the most financial exposure.
✓
Supply chain risk mapping third-party model dependencies, open-source components, data feed vulnerabilities. The SolarWinds attack pattern applied to AI supply chains.
✓
Continuous not point-in-time new models deployed anywhere in the organization are detected and added to the inventory automatically. The BOM stays current without manual effort.
The question your CISO will be asked
"Do you know every AI model running across your organization right now including the ones your teams deployed without telling security?"
What the AI-BOM captures for every model
Model identity & provenance
Name, version, source (HuggingFace, internal, third-party). Training data lineage. Fine-tuning history and who applied it.
Deployment context
Where it runs (on-prem, cloud, edge). What pipelines it's connected to. What agents use it. What data it can access.
Risk posture
OWASP LLM Top 10 coverage gaps. Active vulnerabilities from MITRE ATLAS simulation. BRI financial exposure in dollars.
Compliance status
EU AI Act high-risk classification. GDPR data processing basis. NIST AI RMF posture. ISO 42001 readiness. All auto-assessed.
Supply chain dependencies
Third-party data feeds, open-source components, external API connections. Risk mapped and scored per dependency.
No competing platform offers a full AI-BOM with shadow AI discovery, BRI financial liability per model, and supply chain risk mapping in a single continuous view. IBM watsonx.governance covers partial model inventory for governed models only shadow AI invisible. Azure and AWS cover their own cloud models only.
Discover your full AI attack surface including shadow AI.
Home › AI Security › Compliance Automation
L08 · Compliance Automation · EU AI Act · NIST AI RMF · ISO 42001 · GDPR

Article 9 requires ongoing proof.
A spreadsheet assembled once doesn't qualify.

Article 9 requires documented, lifecycle risk management for high-risk AI systems: evidence that risks are identified, monitored, mitigated, and reviewed over time.

The GRC question every board will ask
"If a regulator requested your AI compliance documentation tomorrow what would you hand them? And can you prove the risk was actively managed, not just documented once?"
Why manual compliance fails the EU AI Act

Article 9 evidence cannot live in a
stale spreadsheet.

Article 9 requires documented evidence that risk management is continuously applied across the AI system lifecycle. Regulators and auditors expect evidence: risk events, decisions, mitigations, remediation actions, and proof the process is maintained.

✓
EU AI Act Art. 9/13/17/53–55 ★ UNIQUE : lifecycle risk evidence, transparency records, quality-management evidence, and audit export, continuously maintained.
✓
NIST AI RMF 1.0 : GOVERN / MAP / MEASURE / MANAGE functions. The US governance standard widely adopted by enterprise and federal environments.
✓
ISO/IEC 42001:2023 : AI management system standard. Continuous certification readiness monitoring. Gap analysis always current.
✓
GDPR AI risk : Art.25 privacy by design, Art.22 automated decision-making, Art.35 DPIA support. All mapped from the live audit trail.
✓
OWASP + MITRE framework mapping : every detected attack automatically mapped to the relevant OWASP LLM and MITRE ATLAS technique. Compliance evidence tied to real security events.
✓
Autonomous compliance updates via the ARE : when a new regulation drops or a compliance gap is detected, the Autonomous Response Engine updates affected layers automatically. No manual re-assessment cycle.
The question no competitor can answer
"Can you show me the continuous risk management evidence for this specific AI system for the last 90 days event by event?"
Frameworks covered all auto-generated, all continuous
EU AI Act (2024/1689)
€30M EXPOSURE
Articles 9, 13, 53–55. Continuous monitoring. Penalty exposure quantified per system. Audit-grade export for conformity assessment under any notified body.
NIST AI RMF 1.0
GOVERN, MAP, MEASURE, MANAGE functions. Continuous posture assessment. Widely adopted by US federal, enterprise, and healthcare environments.
ISO/IEC 42001:2023
AI management system standard. Certification readiness monitoring. Gap analysis continuously updated. Evidence pack for certification audit generated automatically.
GDPR AI Risk + HIPAA + SEC
Art.25 privacy by design. Art.22 automated decisions. Art.35 DPIA support. HIPAA AI data handling. SEC material risk BRI financial liability per AI system for disclosure.
IBM watsonx.governance covers EU AI Act monitoring the only competitor with real compliance capability here. But it requires IBM cloud infrastructure, has no continuous adversarial simulation, and generates no BRI financial liability per system. SAMI covers the same frameworks continuously, works on any infrastructure including air-gapped environments, and ties every compliance event to real security incidents.
Generate your EU AI Act evidence pack. Free assessment.
Home › AI Security › Drift Detection & Gap Analysis
L09 · Drift Detection & Gap Analysis · CTEM for AI · Continuous

The guardrails you calibrated for v1
may be wrong for v2.

Models drift. Behaviour changes between versions, fine-tunes, and deployment environments. A guardrail that worked perfectly on your original model may be over-permissive or over-restrictive on the new one and you won't know until users find the gap. SAMI detects drift and flags it before they do.

Why model updates break security controls silently

Security calibrated for the old model.
Running on the new one.

When your team deploys a new model version, fine-tune, or RLHF update, the model's behaviour distribution shifts. Guardrails calibrated for the old distribution may now be too aggressive (blocking legitimate use cases) or too permissive (missing attack vectors the new model is more vulnerable to). Without continuous drift detection, you find out from user complaints or, worse, from an incident.

✓
Behavioural drift detection ★ UNIQUE SAMI continuously monitors model output distributions and flags significant shifts. New model version detected drift analysis runs automatically before the new version receives production traffic.
✓
Compliance gap analysis identifies regulatory obligations your current controls don't cover. New compliance requirements detected affected layers notified. No manual gap assessment required.
✓
Continuous not periodic drift monitoring runs continuously at runtime, not as a scheduled scan. Real-time gap visibility. No surprises at the next audit.
✓
Feeds directly into the Autonomous Response Engine when drift is detected that creates a compliance or security gap, the ARE automatically updates the affected layers. TrustGuard policy recalibrated. Risk Assessment scores updated.
✓
Shadow model detection new AI models deployed anywhere in the organization are detected, added to the AI-BOM, and drift-monitored from first use.
The question to ask before every model update
"When you update your AI model do your security controls, guardrails, and compliance posture update with it automatically?"
What triggers drift detection and what happens next
🔄 Model version update
New model version or fine-tune deployed. SAMI detects the deployment, runs behaviour comparison against baseline, flags drift above threshold.
→ ARE recalibrates TrustGuard policy bundle for new distribution
📋 New regulatory requirement
EU AI Act implementing act, NIST guidance update, or ISO 42001 revision detected. Gap analysis runs against all AI systems in inventory.
→ ARE updates Compliance Automation monitoring scope
🌐 New attack technique published
Novel jailbreak template, MITRE ATLAS technique, or OWASP guidance update. Existing controls evaluated against new technique.
→ ARE updates Risk Assessment simulation library and reruns
🔍 Shadow AI discovered
New AI model deployment detected across the organization. Added to AI-BOM. Compliance posture assessed. Risk scored.
→ ARE adds to monitoring scope, notifies GRC of new system
No static security tool can do this. Drift detection only works if the monitoring system is continuous, connected to the live security layers, and capable of automatically updating those layers when drift is detected. That requires the Autonomous Response Engine which only SAMI has.
Test drift detection against your current model stack. Free assessment.
⚡ Run Your AI Security Assessment
SAMI AI Security Full Platform

Your AI will be attacked.
The question is whether
you'll know when it happens.

92% of organizations with AI guardrails have already leaked data. Not because they didn't care because the tools they deployed were never designed for what AI attacks actually look like. SAMI was.

⚡ Run Your AI Security Assessment Explore the platform ↓
Before you read this page, consider
92%
of organizations with guardrails have already leaked data. World Economic Forum, 2024.
80%
governance is lagging agent deployment.
Article 9
EU AI Act Article 9 requires lifecycle risk evidence for high-risk AI systems. Penalty exposure is material and phasing in.
WAF, SIEM, and EDR were built for traffic, logs, processes, and signatures. AI attacks also require semantic inspection of prompts, retrieval, responses, and actions.
Home › AI Security › Platform Deep Dive
Home › Contact Us
Get in touch

Talk to the
SAMI team.

Whether you're evaluating AI security options, want to run a proof of value, or have a specific risk question we'll give you a direct, honest answer.

Canada
2275 Upper Middle Rd E, Oakville, ON L6H 0C3, Canada.(Headquarters)
USA
41000 Woodward Ave, Bloomfield Hills, MI 48304, United States.
India
CITUS A Block Phase 1 Olympia Tech Park, Chennai, Tamil Nadu, 600032, India.
Send us a message
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Home › Try It Out
Try it out

Try & Simulate
SAMI Assessments.

Validate and initiate request for multiple assessments. Including External surface assessment, Mobile app. Try out AI security features

Validate & Try
External Attack Surface & AI Security
Scan your external exposure/ Mobile App/ AI Security
Domain + email match required
Home › About Us
About Autnhive

We built SAMI because
AI risk had no answer.

Autnhive was founded on a simple belief: organizations should be able to adopt AI faster without losing control of security, privacy, or compliance.

Devi Narayan
Founder & CEO, Autnhive
A message from our CEO
AI should accelerate human progress without putting people, data, or trust at risk. SAMI exists to make safe, transparent, and controlled AI adoption possible.

AI will become one of the most important forces shaping how organizations operate, compete, serve customers, and make decisions. But AI cannot reach its full potential if organizations are forced to choose between innovation and control.

The future belongs to organizations that can adopt AI quickly, securely, privately, and compliantly. That is why we created SAMI for AI: to accelerate AI adoption by making every AI interaction safer, more private, and compliant by design.

SAMI gives organizations the control layer they need to discover AI use, protect sensitive data, detect live attacks, manage AI and cyber exposure, enforce privacy and compliance rules, and prove what happened when AI systems are used. We are building technology that helps AI move from experimentation to trusted, enterprise-scale adoption.

But Autnhive is not only a technology company. It is a company built around people and trust.

For our customers, we are building a platform that helps them move faster without creating unnecessary risk. They should not have to choose between AI innovation and security, privacy, or compliance. SAMI is designed to help them adopt AI with confidence.

For our employees, we are building a company where talented people can do meaningful work, solve hard problems, and take pride in creating technology that matters. Our team is the foundation of everything we build.

For our investors, we are building a company with the ambition, discipline, and market opportunity to become a global leader in AI security, privacy, compliance, and continuous threat exposure management.

For our partners, we are building an ecosystem that helps bring safe, secure, and compliant AI adoption to more organizations, industries, and communities.

We believe trusted AI will define the next generation of business. Autnhive exists to make that future possible.

Devi
mission

To create the most consequential security, privacy, and compliance platform for AI,
enabling faster and safer AI adoption for everyone.

Visibility first.

You cannot secure what you cannot see. SAMI shows who is using AI, what models are doing, what data they touch, which agents act, and where exposure exists in real time what are the vulnerabilities in your AI systems have and helping to fix them.

Risk in business terms.

AI risk cannot stay buried in technical scores. SAMI translates exposure into financial, regulatory, and operational impact so security leaders, executives, insurers, and boards can act.

Protection at runtime.

Finding risk is not enough. SAMI helps enforce policies, block unsafe actions, redact or transform sensitive data, stop attacks, and keep AI aligned with security, privacy, and compliance requirements.

Closed, not just found.

The industry often measures security by what gets discovered. We measure it by what gets fixed. SAMI helps teams manage AI risk all the way to closure.

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Careers at Autnhive

Build the platform that
makes AI risk visible.

We are a small team solving a real problem at the intersection of AI, security, and financial risk quantification. If that is where you want to be working, read on.

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Why Autnhive
Real problem, real urgency

The AI security gap is not hypothetical. Every week brings a new incident. You will be working on something that matters and is needed now.

Small team, large ownership

Every person owns significant surface area and sees their work in production quickly.

Distributed from day one

Teams across India, Canada, and the US. Remote-first with intentional collaboration.

Do not see a role that fits? We are always interested in people who care about AI security, risk quantification, or developer tooling. Send a note directly

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SAMI AI Assistant
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US officials confirmed AI models can now exploit critical infrastructure. Want me to explain what that means for your organization and how SAMI defends against it?
Pentagon warning?
NeMo vs SAMI?
What is BRI?
EU AI Act?
RAG poisoning?
Zero telemetry?
SIEM integration?
Insurance calc?
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