Audit trails for AI actions that IT can actually query

Schema

User, role, workflow ID, model ID, prompt hash, retrieval snapshot ID, output hash, human decision.

Retention

Align with customer contracts—do not over-store PII.

Drill

Quarterly game day: reconstruct a story from logs in under 30 minutes.

Culture

Reward teams that improve log usefulness, not log volume.

Evidence chains, not screenshots

An AI audit trail should reconstruct decisions: inputs, tool calls, model ID, temperature (if used), policy version, and human overrides in immutable order. Screenshots are not audit trails—they are anecdotes.

Use append-only logs with tamper-evident storage for regulated workflows; allow redaction views for customer data without deleting lineage.

Retention that matches reality

Align retention windows to legal holds and product analytics needs. “Keep everything forever” creates GDPR problems; “keep nothing” creates fraud investigations problems.

Document who can access raw traces versus aggregated metrics.

Cross-system correlation

Propagate a single trace ID across gateway, model host, vector DB, and ticketing. Investigations stall when IDs do not join.

Test join paths quarterly with scripted incidents; broken joins are silent until subpoenas arrive.

Readiness reviews

Before external launch, walk internal audit through one complete story from user click to archived log entry.

SignalSpring’s compliance stance: if you cannot replay it, you cannot defend it.