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AI Audit Trails: Evidence, Scope, and Retention

Design agent audit records for inputs, tool calls, approvals, outcomes, retention, and access while validating storage guarantees.

TraceableClaims should map back to retained source evidence

What to Record

Useful records include actor and tenant context, task identifiers, input references, model or policy version, tool requests, provider results, approval decisions, timestamps, and final status.

Protect Sensitive Data

Reasoning, prompts, provider payloads, and documents can contain secrets or personal data. Apply minimization, masking, access control, regional and retention policy, and deletion handling.

Do Not Overstate Storage

Immutability, WORM behavior, completeness, exportability, and retention duration depend on the configured storage and operations. Validate them against deployment evidence and legal requirements.

Frequently Asked Questions

Is every internal reasoning token available?

No universal guarantee is made. Record the evidence needed for accountability without exposing hidden or sensitive provider data.

Is retention always seven years?

No. Retention is deployment and policy configuration and must follow applicable obligations.

Topics

AI audit trailagent loggingAI decision evidenceaudit retention

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