Log Analysis for AI Safety Incidents

When an AI agent safety incident occurs, the audit trail and monitoring logs are your primary investigative tools. Effective log analysis determines what happened, when, why, and what to do about it. This guide covers practical techniques for incident investigation.

Establish the Timeline

Start by identifying the time window of the incident. Pull all audit receipts from that period:

npx authensor receipts list --from "2026-02-18T10:00:00Z" --to "2026-02-18T11:00:00Z" --agent <agent-id>

Sort receipts chronologically. The hash chain preserves ordering, so you can verify that no receipts have been inserted or removed after the fact.

Identify the Trigger

Look for the first anomalous action in the timeline. Common triggers include:

• A policy evaluation that returned "allow" for an action that should have been denied
• A content scan that missed a prompt injection
• A tool call with parameters outside expected ranges
• An approval that was granted inappropriately

The receipt for each action includes the policy rule that matched, the Aegis scan result, and the approval decision (if applicable). This metadata narrows the investigation.

Trace the Chain of Actions

Agent incidents rarely involve a single action. More often, a sequence of individually permissible actions produces a harmful outcome. Map the full sequence:

1. What did the agent do before the incident?
2. What information did it retrieve?
3. How did retrieved information influence subsequent actions?
4. Was there a pattern of escalation (gradually increasing scope or privilege)?

Analyze Content Scanner Results

For each action in the incident window, review the Aegis scan results:

• Were any detections flagged but overridden?
• Were detection confidence scores near the threshold?
• Did the content use obfuscation techniques that bypassed scanning?

Correlate with Sentinel Metrics

Check whether Sentinel flagged any behavioral anomalies during the incident window:

• Action rate deviations
• Tool usage distribution changes
• Error rate increases
• Session length anomalies

If Sentinel did not alert, determine whether the anomalous behavior was within the baseline's expected range. If it was, the baseline needs recalibration.

Verify Chain Integrity

Before drawing conclusions from the audit trail, verify the hash chain:

npx authensor verify-chain --from <first-receipt> --to <last-receipt>

A broken chain means the records may have been tampered with. Document this finding and treat the evidence accordingly.

Preserve Evidence

Export all relevant logs, receipts, and metrics to an immutable store before making any changes to the system. Policy updates, threshold changes, and agent modifications should wait until the investigation evidence is secured.

Effective log analysis transforms raw data into actionable understanding. The quality of your incident response depends directly on the quality of your investigation.