If you're mid-Type-II and your auditor is now asking for a prompt-injection red-team trail, AI risk assessment, or evidence your LLM features can't be manipulated into leaking data, Sentinel Scan generates exactly that: a transcript-backed report mapped to the OWASP LLM Top 10 and NIST AI 600-1, ready to hand to your auditor. Same evidence also answers the AI modules now showing up in SIG Core/Lite and CSA AI-CAIQ procurement questionnaires, and the model-lineage or inference-log-redaction questions that come with them.
No signup to try it: npx sentinel-scan-cli --demo or pipx install sentinel-scan-cli && sentinel-scan --demo · see an unedited sample report first → · see the full Annex IV/SOC2-mapped evidence pack →
Deal stuck in a security review instead of an audit? Jump to the procurement-questionnaire path ↓. Already had an incident? Skip to the incident-response fast path ↓.
Auditors covering SOC 2 Type II engagements are increasingly attaching an AI-specific addendum once a company's product ships an LLM feature, chat, copilot, agent, RAG search, anything user input reaches a model. There's no separate "SOC 2 for AI" report yet, so auditors map the existing five Trust Services Criteria onto AI-specific failure modes instead: model lineage (what model, what version, what fine-tune, and when it changed), inference log handling and PII redaction, drift monitoring, third-party LLM vendor risk assessment, and evidence of adversarial testing against prompt injection and jailbreak attempts. Generic SOC 2 controls (access control, change management, encryption) don't produce that last piece on their own, because they were never designed to test what happens when an attacker talks to your model instead of your API.
This isn't hypothetical pressure from auditors alone. 2026 has had a steady drumbeat of real MCP and agentic-AI breaches: the Asana MCP server flaw exposed roughly 1,000 organizations to cross-tenant data access before it was patched, and the GitHub MCP server had a prompt-injection path that let a malicious issue exfiltrate private repository data. Security researchers have also tracked 30+ MCP-related CVEs in a single 60-day window, with command injection behind roughly 43% of them. Auditors and procurement reviewers read the same headlines you do, which is why the AI addendum and questionnaire modules exist now.
That's a narrow, specific gap, and it's usually discovered late: during fieldwork, a few weeks before the report is due, when there's no time to build a red-team program from scratch. Sentinel Scan exists to close that gap fast: run the attack corpus, get the transcripts and a written report, hand it to your auditor as evidence for the control. It doesn't produce your model-lineage documentation or your inference-log-redaction policy (those live in your own data governance controls), but it's exactly the adversarial-testing evidence those controls are usually missing.
| Auditor asks for | Sentinel Scan deliverable |
|---|---|
| Evidence of adversarial / red-team testing on AI features | 15+ attack corpus (direct override, jailbreak/roleplay, fake system tags, encoding tricks, indirect/RAG injection) run against your live or staging target, full transcripts retained |
| Independent judgment, not self-attestation | Every response scored by a separate LLM judge for actual policy violation or data leakage, not a keyword filter you control |
| Mapping to a recognized framework | Findings tagged to OWASP LLM Top 10 and cross-referenced to NIST AI 600-1 categories, the same references most auditors already recognize |
| A written artifact to attach to the audit package | PDF/markdown report: what was tested, what broke (if anything), root cause, remediation guidance, dated and reproducible |
| Re-test after remediation | One free re-test included in the managed audit once you've shipped fixes, so the evidence trail shows closure, not just a finding |
| SIG Core/Lite or CSA AI-CAIQ questionnaire: "adversarial testing / red-team evidence for AI features" line item | Same report, attached directly as the answer artifact; we map findings to OWASP LLM Top 10 and reference NIST AI 600-1 / ISO 42001 language reviewers already recognize |
Note: this covers the adversarial-testing line items. Model lineage documentation and inference-log PII redaction policy are questions your own data governance controls need to answer; we don't generate those.
Compliance-tool users: we're not a Vanta or Drata competitor, we generate the AI-specific evidence artifact you upload into whichever platform your auditor already uses.
Enterprise procurement now routinely runs vendors through Shared Assessments SIG (Lite, ~128 questions, or Core, ~627 questions) with an AI module bolted on, or CSA's AI-CAIQ, both asking for MCP governance and agentic AI controls mapped to OWASP, NIST, ISO 42001, and the EU AI Act. If your deal is stuck because the buyer's security team wants proof you've adversarially tested the LLM feature they'd be relying on, that's the same evidence as the audit path above, just addressed to a procurement reviewer instead of an auditor.
We'll turn around a report you can attach directly to the questionnaire response, with findings tagged to the frameworks their checklist already cites.
If you've already had a prompt-injection or MCP-related incident (or found signs of one) and need remediation evidence fast, for an incident report, a customer, or a re-opened audit, this skips the standard scoping queue. Tell us what happened and we'll get back to you same business day, not the usual one-business-day window.
This is the highest-urgency path we offer: post-incident engagements get priority scheduling ahead of standard managed audits.
Fill this in and we'll reply within one business day with a scoped timeline that fits your audit window.