SOC 2 applies to an AI or LLM product by evaluating controls against the AICPA's trust services criteria of security, availability, processing integrity, confidentiality, and privacy, but the scope has to be extended to cover AI-specific components a traditional audit might otherwise miss. Auditors now routinely ask how prompts and model outputs are logged and secured, whether training or fine-tuning data includes customer information and under what retention policy, how vector databases and embeddings are access-controlled, and which third-party model providers sit in the data flow as subprocessors requiring their own due diligence. A SOC 2 Type II report, which evaluates controls over a period typically between six and twelve months, carries more weight with enterprise buyers than a Type I report confirming controls existed at a single point in time. Companies preparing for their first SOC 2 audit as an AI vendor should map every place personal or confidential data touches a model. Passing SOC 2 does not by itself satisfy AI-specific regulations like the EU AI Act, but it demonstrates operational security maturity that regulators and customers both value. Nanobase AI, an NVIDIA Inception Program member, helps AI product teams scope SOC 2 controls around their model and data pipeline before an audit begins.
The framework did not change, but the questions did
SOC 2 evaluates an organization's controls against the AICPA's trust services criteria: security, availability, processing integrity, confidentiality, and privacy. None of these five criteria were rewritten for AI, which means a SOC 2 audit for an LLM application uses the same overall framework as any other SaaS audit. What changed is the specific set of questions auditors now ask within that framework, since an AI product introduces components, like prompt logs and vector databases, that a traditional web application audit scope would not otherwise cover. Treating SOC 2 for an AI product as "the same audit, expanded scope" is more accurate than treating it as a new or separate certification track.
Where auditors now probe specifically
| Trust services criterion | AI-specific question an auditor is likely to ask |
|---|---|
| Security | How are prompts and model outputs logged, and are logs access-controlled? |
| Confidentiality | Does fine-tuning or training data include customer information, and under what retention policy? |
| Processing integrity | Is there monitoring for model output quality or drift, and a process for handling incorrect outputs? |
| Privacy | Is customer data used to train or fine-tune any model without explicit authorization? |
| Availability | What is the fallback behavior if the underlying model provider's API is unavailable? |
Vector databases and embeddings, often overlooked because they do not look like traditional "customer data" storage, increasingly appear as a distinct control area since they can encode sensitive information from source documents even without storing the documents verbatim. Auditors now scope prompts, model outputs, and vector stores into the review, so treating them as outside the traditional data-store control boundary is a gap waiting to be found.
Building the control set before the audit starts
- Document the full data flow through the AI system, including any third-party model providers, embedding services, and vector stores, the same mapping exercise useful for GDPR compliance.
- Establish and enforce a policy on whether customer data is ever used for model training or fine-tuning, with technical controls, not just a policy statement, backing it up.
- Implement logging and monitoring for prompts, outputs, and any tool calls the AI system makes, with defined retention and access control.
- Document vendor due diligence for every model and infrastructure provider in the chain, since SOC 2 examines sub-processor risk as part of the overall control environment.
- Establish an incident response process that specifically covers AI failure modes, such as a prompt injection incident or an unexpected data exposure through model output.
A company that treats prompt logs and vector stores with the same rigor it already applies to its production database is usually most of the way to satisfying an auditor's AI-specific questions.
Type I versus Type II for a newer AI feature
A SOC 2 Type I report attests that controls are suitably designed at a point in time, while a Type II report attests that controls operated effectively over an observation period, typically three to twelve months. For a newly launched AI feature, a Type I report can demonstrate a sound control design quickly, which is often what enterprise prospects need to move a deal forward, while a Type II report built up over subsequent audit cycles demonstrates sustained operation. A Type I report can unblock a deal quickly, but enterprise buyers increasingly expect a Type II report as the AI feature matures past its first release cycle. This is general information, not legal or audit advice, and the appropriate report type and scope should be discussed with a qualified SOC 2 auditor.
Frequently asked questions
Does using a third-party LLM API instead of a self-hosted model change SOC 2 scope?
It changes where certain controls live rather than removing them. A third-party API becomes a sub-processor subject to vendor due diligence review, while a self-hosted model shifts more control ownership, and more audit evidence generation, directly onto the company's own infrastructure.
Do we need a separate AI-specific certification alongside SOC 2?
Not necessarily. Some enterprise customers now also ask about ISO 42001 specifically for AI governance, covered in our guide to ISO 42001 certification, but many customers accept an expanded-scope SOC 2 report that explicitly addresses AI components.
How does prompt injection fit into a SOC 2 audit?
It typically falls under the security criterion as an application-layer risk, and auditors increasingly expect evidence of testing for it, similar to how penetration testing is expected for traditional web application security within a SOC 2 scope.
Can a startup with a single AI feature get SOC 2 certified quickly?
Yes, scope can be limited to the specific system and features in question rather than the whole company, which is a common approach for startups needing a SOC 2 report to close enterprise deals without auditing unrelated internal systems.
How Nanobase AI helps
Nanobase AI, headquartered in Silicon Valley, helps AI product teams map their prompt, model, and vector database data flows into a SOC 2-ready control set, and builds the logging, retention, and vendor due diligence documentation auditors expect from a modern LLM application. This is part of our AI security and compliance practice, and it complements our MCP and API integration work for companies connecting AI systems to enterprise data sources.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.