AI Agent Incident Response: A Playbook for Engineering Teams

AI agent incidents — unauthorized file access, data exfiltration attempts, runaway cost accumulation, or prompt injection exploits — require a structured response process adapted for autonomous systems. SafeClaw by Authensor provides the detection and forensic foundation: its hash-chained audit logs capture every action evaluation with tamper-proof integrity, enabling teams to detect anomalies, trace root causes, contain the blast radius, and recover with confidence.

Quick Start

npx @authensor/safeclaw

The Four-Phase Playbook

Phase 1: Detection

AI agent incidents are detected through three channels:

Automated monitoring — watch for anomalous deny patterns:

npx @authensor/safeclaw audit --filter effect=deny --watch --alert-threshold 10

This alerts when denied actions exceed 10 per minute, indicating an agent attempting unauthorized operations.

Budget alerts — cost spikes signal runaway behavior:

alerts:
  - trigger: budget.warn
    threshold: "80%"
    channel: slack
    message: "Agent spend at {percent}% — investigate"

Manual review — periodic audit log review catches subtle patterns:

npx @authensor/safeclaw audit summary --since "24h"

Look for unusual action types, unexpected file paths, or access patterns outside business hours.

Phase 2: Containment

Once an incident is detected, contain it immediately:

Immediate kill switch — switch to a fully restrictive policy:

# .safeclaw/emergency-lockdown.yaml
version: "1.0"
description: "Emergency lockdown — all actions denied"

rules:
  - action: "*"
    effect: deny
    reason: "INCIDENT RESPONSE: Emergency lockdown active"

Apply it:

SAFECLAW_POLICY=emergency-lockdown.yaml npx @authensor/safeclaw

Selective containment — if you know the scope, restrict only the affected agent or action type:

# Disable network access while keeping file operations
rules:
  - action: network.request
    domain: "*"
    effect: deny
    reason: "INCIDENT: Network access suspended pending investigation"

- action: file.read
    path: "src/**"
    effect: allow
    reason: "Read-only access during containment"

- action: "*"
    effect: deny

Phase 3: Analysis

Use SafeClaw's audit trail to reconstruct the incident timeline:

# Export all actions during the incident window
npx @authensor/safeclaw audit export \
  --since "2026-02-13T14:00:00Z" \
  --until "2026-02-13T15:30:00Z" \
  --format json > incident-timeline.json

Verify log integrity (confirm logs were not tampered with)
npx @authensor/safeclaw audit verify \
  --since "2026-02-13T14:00:00Z" \
  --until "2026-02-13T15:30:00Z"

Key questions to answer:

1. What triggered the incident? — First anomalous action in the timeline
2. What was the blast radius? — All allowed actions between detection and containment
3. Were any deny rules bypassed? — Hash chain integrity check confirms
4. Which policy was active? — Log entries include the active policy name
5. Was the agent prompt-injected? — Review the action sequence for uncharacteristic patterns

Phase 4: Recovery

After analysis, restore operations with updated policies:

# Updated policy with patches for the vulnerability
version: "1.0"
description: "Post-incident policy — patched"

rules:
  # New rule: block the attack vector
  - action: network.request
    domain: "malicious-endpoint.example.com"
    effect: deny
    reason: "POST-INCIDENT: Blocked exfiltration endpoint"

# Tighter file access
  - action: file.read
    path: "src/**"
    effect: allow

- action: file.write
    path: "src/**"
    effect: allow
    excludePaths:
      - "src/config/**"

- action: "*"
    effect: deny

Incident Report Template

Document the incident using SafeClaw evidence:

## Incident Report: [ID]

Detected: [timestamp] via [detection method]
Contained: [timestamp] via [containment action]
Root Cause: [description]
Blast Radius: [affected files/systems]
Audit Log Integrity: VERIFIED / COMPROMISED
Policy Active at Time: [policy filename]
Actions During Incident: [count allowed] allowed, [count denied] denied
Remediation: [policy changes applied]
Preventive Measures: [new rules added]

Post-Incident Policy Hardening

After every incident, review and tighten policies:

1. Add explicit deny rules for the attack vector
2. Reduce scope of existing allow rules
3. Add rate limits if the incident involved high-frequency actions
4. Enable additional monitoring for the affected action types
5. Run npx @authensor/safeclaw validate to verify updated policy syntax

Why SafeClaw

• 446 tests ensure audit trail reliability during forensic investigation
• Deny-by-default limits blast radius — unauthorized actions are blocked, not just logged
• Sub-millisecond evaluation means real-time containment via policy swap
• Hash-chained audit trail guarantees forensic log integrity
• Works with Claude AND OpenAI — incident response applies across all agent providers
• MIT licensed — full source access for security team review

See Also

• AI Agent Compliance Reporting: What Auditors Need
• Audit Trail Requirements for AI Agents in Regulated Industries
• Zero Trust Architecture for AI Agents
• Building an AI Governance Framework with SafeClaw