Introduction
AI systems require specialized incident response procedures that address their unique characteristics. Traditional IR frameworks must be adapted to handle AI-specific incidents such as model compromise, adversarial attacks, and data poisoning.
This part covers AI incident response planning, detection, containment, eradication, recovery, and lessons learned, aligned with NIST and industry frameworks.
💡 AI Incident Categories
AI incidents fall into several categories requiring tailored responses: model compromise (backdoors, tampering), adversarial attacks (evasion, manipulation), data incidents (poisoning, theft), extraction attacks (model theft), prompt injection (LLM manipulation), and AI misuse (unauthorized use, deepfakes).
AI IR Framework
The AI incident response lifecycle follows the standard phases while incorporating AI-specific considerations at each stage.
Preparation
Establish AI-specific IR capabilities before incidents occur.
Key Actions: AI asset inventory, IR playbooks, team training, tool deployment, forensic capabilities
Detection & Analysis
Identify and analyze AI-specific security events.
Key Actions: Model monitoring, anomaly detection, triage, impact assessment, evidence preservation
Containment
Limit the impact and prevent further damage.
Key Actions: Model isolation, API restrictions, traffic filtering, access revocation
Eradication
Remove the threat and compromised components.
Key Actions: Model replacement, data cleansing, backdoor removal, credential rotation
Recovery
Restore AI systems to normal operation.
Key Actions: Model redeployment, validation testing, monitoring enhancement, gradual restoration
Lessons Learned
Improve defenses based on incident experience.
Key Actions: Root cause analysis, documentation, control improvements, playbook updates
Detection & Analysis
Detecting AI incidents requires monitoring for AI-specific indicators beyond traditional security telemetry.
| Incident Type | Detection Indicators | Analysis Focus |
|---|---|---|
| Model Compromise | Unexpected predictions, performance changes, backdoor triggers | Model integrity, weight comparison, behavior analysis |
| Adversarial Attack | Unusual inputs, high-confidence errors, evasion patterns | Input analysis, perturbation detection, attack characterization |
| Data Poisoning | Training anomalies, class-specific degradation | Data integrity, sample analysis, poison identification |
| Model Extraction | Query volume anomalies, systematic probing | Query pattern analysis, IP theft assessment |
| Prompt Injection | Unusual outputs, instruction following anomalies | Input parsing, jailbreak analysis, data exfiltration |
📜 AI Incident Triage Questions
- Which AI system(s) are affected? What is their business criticality?
- Is the model still in production? What decisions is it making?
- Is the attack ongoing or completed? Is there evidence of persistence?
- What is the potential impact - safety, financial, reputational?
- Are other AI systems potentially affected (shared data, models, infrastructure)?
- What evidence needs immediate preservation?
Containment Strategies
Containment for AI incidents must balance stopping the attack with maintaining business operations where safe.
💀 Containment Options
- Model Isolation: Take compromised model offline entirely
- Failover: Switch to backup model or previous version
- API Restrictions: Rate limit, block suspicious sources, require authentication
- Input Filtering: Block adversarial patterns at ingestion
- Output Suppression: Restrict model outputs pending review
- Human-in-the-Loop: Route all decisions through human review
Immediate Isolation (High Priority):
• Safety-critical AI (medical, autonomous vehicles)
• Confirmed backdoor or model compromise
• Active data exfiltration
Failover to Backup (Medium Priority):
• Significant performance degradation
• Suspected data poisoning
• Ongoing extraction attack
Restricted Operation (Lower Priority):
• Limited adversarial activity
• Non-critical AI system
• Business-critical with fallback options
Eradication & Recovery
Eradication removes the threat completely, while recovery restores AI systems to trusted operation.
📋 Eradication Actions by Incident Type
- Compromised Model: Replace with clean version; if backdoored, retrain from verified data
- Data Poisoning: Identify and remove poisoned samples; retrain model; validate clean data
- Extraction Attack: Rotate API keys; implement enhanced protections; consider model watermarking
- Prompt Injection: Patch vulnerable prompts; implement input sanitization; add guardrails
- Credential Compromise: Rotate all affected credentials; review access logs
⚠ Recovery Validation
Before returning AI to production, validate: model integrity (compare weights/behavior to known-good), performance metrics (accuracy, fairness on validation set), security posture (all vulnerabilities addressed), monitoring (enhanced detection in place), and gradual rollout (start with limited traffic, expand after validation).
| Recovery Phase | Actions | Validation |
|---|---|---|
| Pre-deployment | Security review, testing, stakeholder approval | Penetration testing, code review, sign-off |
| Limited Deployment | Deploy to subset, enhanced monitoring | Performance metrics, anomaly detection |
| Expanded Deployment | Gradual traffic increase | Continued monitoring, comparison to baseline |
| Full Production | Normal operation with enhanced controls | Ongoing monitoring, periodic review |
AI IR Playbooks
Pre-defined playbooks enable rapid, consistent response to AI incidents.
Trigger: Automated detection of trigger pattern in model behavior OR analyst suspicion
Immediate Actions (0-15 min):
1. Alert IR team and AI team lead
2. Capture model state and logs
3. Assess criticality and blast radius
Short-term Actions (15-60 min):
4. Isolate model or failover to backup
5. Begin backdoor analysis
6. Review access logs for unauthorized changes
7. Notify stakeholders
Investigation (1-24 hrs):
8. Conduct neural cleanse analysis
9. Compare to known-good model version
10. Identify backdoor trigger pattern
11. Trace insertion point (training data, supply chain)
Remediation:
12. Deploy clean model version
13. If no clean version, retrain from verified data
14. Implement detection for trigger pattern
15. Document and close incident
Legal & Regulatory Considerations
AI incidents may trigger legal and regulatory obligations that must be addressed during response.
📜 Notification Requirements
- Data Breach: If AI incident involves personal data exposure, GDPR/CCPA notification may apply
- EU AI Act: Serious incidents involving high-risk AI must be reported to authorities
- Sector Regulations: Financial, healthcare AI may have specific reporting requirements
- Contractual: Customer contracts may require incident notification
- Voluntary: Consider disclosure for transparency and community warning
⚠ Evidence Preservation
AI incident evidence requires careful preservation for potential litigation or regulatory investigation: model artifacts (weights, configurations at time of incident), training data (if poisoning suspected), input/output logs (attack patterns, affected decisions), access logs (who accessed AI systems), and chain of custody documentation.
Key Takeaways
- AI-Specific IR: Adapt traditional IR frameworks for AI-unique challenges
- Preparation: Develop playbooks, train teams, deploy AI-specific detection
- Detection: Monitor for AI-specific indicators beyond traditional security
- Containment: Balance business continuity with risk; consider failover to backup models
- Eradication: May require model replacement or retraining; verify clean state
- Recovery: Validate thoroughly before returning to production; gradual rollout
- Legal Obligations: Assess notification requirements; preserve evidence properly