SOC 2 for AI Companies

Traditional SOC 2 controls don't account for probabilistic AI models. Here is how to bridge the gap and pass your audit with flying colors.

The AI Gap in SOC 2

SOC 2 (Service Organization Control 2) focuses on Security, Availability, Processing Integrity, Confidentiality, and Privacy.

However, standard controls like "Change Management" break down when "code" becomes "weights and biases". How do you prove that a model update didn't introduce a security vulnerability?

New Controls for AI

To satisfy auditors for an AI-native product, you need to add specific controls to your Trust Services Criteria (TSC).

1. Data Training Security (Confidentiality)

Control: "The organization ensures that customer data is not used for model training without explicit consent."

Evidence: Logs showing data segregation in your vector database (e.g., Pinecone namespaces) and RBAC policies preventing cross-tenant data leakage.

2. Model Determinism (Processing Integrity)

Control: "The organization monitors AI outputs for hallucinations and accuracy drift."

Evidence: Automated evaluation reports (evals) run against a "Golden Dataset" before every deployment.

3. Supply Chain Risk (Security)

Control: "Third-party AI models (e.g., OpenAI, Anthropic) are vetted for security and availability."

Evidence: Vendor risk assessments and fallback mechanisms (e.g., switching to Azure OpenAI if the main API goes down).

How Railguard Helps

Railguard provides the automated evidence you need for your SOC 2 audit.