Quarterly AI Agent Policy Review Checklist

AI agent capabilities evolve, team members change, and threat landscapes shift. A quarterly policy review ensures your SafeClaw deny-by-default policies remain accurate, minimal, and aligned with current requirements. SafeClaw by Authensor provides the policy engine and audit data that drive this review. If you have not yet installed SafeClaw, start with npx @authensor/safeclaw.

Pre-Review Preparation

• ✅ 1. Export the current policy file. Pull the latest safeclaw.config.yaml from version control and have it accessible during the review.

• ✅ 2. Export the last quarter's audit trail summary. Generate a summary of all action types, frequencies, and decisions from the past 90 days.

• ✅ 3. Gather policy change history. Review all git commits that modified the policy file in the last quarter. Note who made each change and why.

• ✅ 4. Identify team changes. Note any developers who joined, left, or changed roles since the last review.

• ✅ 5. Review any incidents from the quarter. Pull incident reports related to AI agent behavior and note any policy changes made in response.

Rule-by-Rule Review

• ✅ 6. Verify every allow rule is still needed. For each allow rule, confirm the agent still requires that permission. Remove rules for deprecated features or retired agents.

# Ask for each allow rule:
- Is the agent still using this permission?
- Is the scope still minimal?
- Can the path/command/domain be narrowed further?
  - action: file.write
    path: "/app/output/**"
    decision: allow
    reason: "Agent output directory — REVIEWED Q1 2026"

• ✅ 7. Check for overly broad rules. Look for wildcard patterns that could be narrowed. /app/ might be reducible to /app/output/ based on actual usage in the audit trail.

• ✅ 8. Verify escalation rules match current risk assessment. Actions that were previously low-risk may have become high-risk (e.g., a new database containing PII). Update escalation rules accordingly.

• ✅ 9. Confirm deny rules still cover known threats. Review the deny rules for credential access, destructive commands, and unauthorized network targets. Add rules for any new threat patterns identified in the quarter.

• ✅ 10. Validate rule ordering. SafeClaw uses first-match-wins. Verify that more specific rules appear before more general rules to prevent unintended matches.

Audit Trail Analysis

• ✅ 11. Review denied action patterns. High-frequency denied actions may indicate:

• ✅ 12. Identify unused allow rules. If an allow rule has zero matches in the audit trail for 90 days, consider removing it. Unused permissions are unnecessary attack surface.

• ✅ 13. Check for new action types. If the agent started performing action types not seen in previous quarters, verify these are expected and covered by appropriate rules.

• ✅ 14. Verify hash chain integrity. Run npx @authensor/safeclaw --verify-audit to confirm the audit trail has not been tampered with since the last review.

• ✅ 15. Review escalation outcomes. Analyze which escalations were approved vs. rejected. Consistently approved escalations may warrant an allow rule. Consistently rejected ones may warrant a deny rule.

Compliance and Documentation

• ✅ 16. Update policy documentation. Ensure every rule has a current reason field and that the policy file's header comments reflect the latest review date.

• ✅ 17. Record the review outcome. Document the review date, participants, rules added, rules removed, rules modified, and rationale for each change.

• ✅ 18. Commit the updated policy. Push the reviewed policy to version control with a commit message referencing the quarterly review.

• ✅ 19. Update compliance evidence. If the policy review satisfies compliance controls (SOC 2 CC6.2, ISO 27001 A.9.2.5), update the compliance documentation.

• ✅ 20. Schedule the next review. Set the calendar invite for the next quarterly review with all required participants.

Cross-References

• AI Agent Compliance Audit Checklist
• Policy Design Principles
• Monitoring and Tuning
• Policy Rule Syntax Reference