What belongs in the trace
A useful trace captures user intent, retrieved sources, tool calls, policy checks, approval events, generated output, and final disposition.
Prompt and context
Source excerpts
Tool execution
Reviewer action
Auditability
AI Audit Trails
AI audit trails preserve the prompts, sources, tool calls, approvals, outputs, model versions, and reviewer decisions behind important AI-assisted work.
Design for review
Audit records should be searchable by workflow, user, source, risk level, and outcome.
Run IDs
Risk labels
Source IDs
Exportable records
Use traces operationally
Traces help teams find weak sources, tune policies, investigate incidents, and prove review behavior.
Incident review
Quality improvement
Compliance sampling
Training data boundaries
Related reading
Continue through the connected solution pages, case studies, and planning references.
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