AI audit trails: what to log
Audit trails are about evidence, not exhaust. A useful AI audit trail records decisions and policy context without storing unnecessary raw content. This keeps reviews credible and data handling lean.
Start with decision metadata
- Request ID
- Decision: allow, deny, or approval_required
- Policy reference or version
- Timestamp (UTC)
- Reviewer metadata for approvals (optional)
These fields create a traceable story of what happened and why. They are also easy to review during audits without exposing raw prompts or outputs.
Why evidence-first beats full storage
Full-content logging expands the data surface and complicates retention policies. Evidence-first logging keeps the focus on decision context. If deeper content retention is required for a pilot, it should be explicitly documented and agreed.
How to keep logs reviewable
Log entries should map to a policy version and include timestamps. That makes it possible to reconstruct what the system did without guessing. It also supports incident reviews where timing and policy context matter more than raw content.
Pilot checklist
- Confirm that every gated decision writes an audit record.
- Review a sample of log entries with stakeholders.
- Define retention duration and access scope.
- Document any exceptions or additional content storage.
Related docs
Next step
If you want to validate audit coverage in a pilot, share the use case and review requirements. We respond with a scoped plan and clear evidence outputs.