AI compliance
A practical SOC 2 guide for AI apps and LLM startups: trust services criteria, AI-specific controls, model changes, prompt logs, data retention, RAG permissions, evals, and vendor evidence.
Comparison
| Option | Best for | Strengths | Tradeoffs | Use when |
|---|---|---|---|---|
| Standard SaaS SOC 2 controls | Access control, change management, monitoring, incident response, vendor management, and infrastructure security | Builds the baseline enterprise trust layer every AI startup still needs. | Does not automatically explain model behavior, prompts, RAG, evals, or AI-specific abuse cases. | The AI app is also a normal cloud service storing or processing customer data. |
| AI-specific control evidence | Prompt logs, model changes, evals, red teaming, RAG permissions, retention, and generated output risk | Answers buyer concerns that generic cloud controls do not cover. | Needs engineering instrumentation and product governance, not only policy documents. | The product uses LLMs, tools, agents, embeddings, or customer knowledge bases. |
| Regulated workflow controls | Health, finance, legal, employment, education, or other high-impact AI workflows | Adds human review, domain validation, audit trails, and stricter data controls. | May require additional frameworks, legal review, and customer-specific commitments. | The AI output can affect rights, safety, money, or regulated decisions. |
Enterprise buyers want to know how the AI behaves, but auditors and security teams need evidence. Convert model and product behavior into policies, logs, tests, approvals, and change records.
AI apps often process sensitive prompts, files, outputs, embeddings, transcripts, and support logs. SOC 2 readiness needs a clear story for retention, encryption, access, deletion, and customer control.
A model upgrade can change product behavior as much as a code release. Make model, prompt, and retrieval changes part of change management instead of informal configuration edits.
Decision Rules
Start with standard SaaS SOC 2 controls, then add AI-specific evidence.
Document prompt, model, retrieval, and tool changes as production changes.
Track prompt/output retention, access, deletion, and training use explicitly.
Use evals and red-team findings as recurring control evidence.
Related Guides
See the questions enterprise buyers are likely to ask.
OpenCollect traces, costs, latency, and quality evidence for operations.
OpenPrepare for the AI-specific questions buyers ask.
OpenUse adversarial testing as security evidence.
OpenHandle storage, processing, retention, and regional controls.
OpenTopic Hubs
Industry Pages
Industry page
Compare AI cybersecurity tools for SOC analysts, SIEM, SOAR, XDR, CNAPP, exposure management, DSPM, DLP, PAM, email security, AI GRC, vendor risk, red teaming, and compliance automation.
OpenIndustry page
Compare AI tools for legal teams, contract review, CLM, eDiscovery, compliance automation, GRC, vendor review, document processing, and high-control review workflows.
OpenIndustry page
Compare AI medical scribes, healthcare documentation tools, document processing, privacy controls, compliance automation, support workflows, analytics, and operational AI for healthcare teams.
OpenFAQ
Many enterprise buyers expect SOC 2 or similar assurance from SaaS vendors, including AI startups. Whether it is required depends on customer, market, data sensitivity, and procurement process.
AI apps need the usual SaaS controls plus evidence for prompts, outputs, embeddings, model changes, evals, red teaming, retrieval permissions, retention, and generated-output risk.
No. SOC 2 is about controls and assurance, not a guarantee of model accuracy. Use evals, monitoring, and domain review to measure AI quality.
Source Links
Reference
AICPA overview of SOC services.
OpenReference
AICPA Trust Services Criteria resource page.
OpenReference
OpenAI security, compliance, and accreditation page.
OpenReference
NIST AI risk-management framework resource page.
OpenThe strongest decision is always local to your workflow. Save the vendor links, define a representative task, record the exact prompt or command, and compare the final evidence instead of the marketing claim.
Return to the AI learning map