TL;DR

AI incidents are different from traditional software incidents—they can involve bias, harmful outputs, or subtle degradation that's hard to detect. Build incident response procedures that address AI-specific failure modes, include diverse responders, and focus on both technical fixes and harm mitigation.

Why it matters

When AI systems fail in production, the consequences can range from minor inconveniences to significant harm. Effective incident response minimizes damage, restores service quickly, and prevents recurrence. Without good procedures, incidents become crises.

AI incident types

Technical failures

System doesn't work as expected:

  • Service unavailable
  • Latency degradation
  • Integration failures
  • Resource exhaustion

Detection: Traditional monitoring (uptime, latency, error rates)

Quality degradation

System works but outputs degrade:

  • Increased error rates
  • Model drift
  • Decreased relevance
  • Inconsistent responses

Detection: Output quality monitoring, user feedback

Harmful outputs

System produces problematic content:

  • Biased decisions
  • Toxic or inappropriate content
  • Privacy violations
  • Dangerous information

Detection: Content moderation, user reports, audits

Security incidents

System is compromised or abused:

Detection: Security monitoring, anomaly detection

Incident response framework

Phase 1: Detection

Monitoring sources:

  • Automated alerts (technical metrics)
  • Quality monitoring systems
  • User reports and feedback
  • Social media monitoring
  • Internal testing

Severity classification:

Severity Impact Response time
Critical Widespread harm, safety risk Immediate
High Significant user impact Within 1 hour
Medium Limited impact, workarounds exist Within 4 hours
Low Minor issues Within 24 hours

Phase 2: Triage

Initial assessment:

  • What is happening?
  • How many users affected?
  • Is there ongoing harm?
  • What's the scope?
  • Is it getting worse?

Key decisions:

  • Escalation needed?
  • Immediate mitigation required?
  • Who needs to be notified?
  • Public communication needed?

Phase 3: Containment

Mitigation options:

  • Disable affected feature
  • Rollback to previous version
  • Activate fallback system
  • Rate limit or throttle
  • Add guardrails

Communication:

  • Notify stakeholders
  • Update status page
  • Prepare user messaging
  • Brief support teams

Phase 4: Resolution

Technical remediation:

  • Identify root cause
  • Develop and test fix
  • Deploy fix safely
  • Verify resolution

Harm remediation:

  • Identify affected users
  • Assess impact
  • Determine remediation needs
  • Execute remediation plan

Phase 5: Post-incident

Immediate:

  • Confirm resolution
  • Update stakeholders
  • Close incident

Follow-up:

  • Conduct post-mortem
  • Document lessons learned
  • Implement preventive measures
  • Update procedures

AI-specific considerations

Handling bias incidents

When AI shows discriminatory behavior:

Immediate actions:

  • Document the behavior
  • Assess scope and impact
  • Consider disabling until fixed
  • Notify compliance/legal

Investigation:

  • Analyze affected decisions
  • Review training data
  • Check for systematic patterns
  • Consult affected communities

Remediation:

  • Correct affected decisions
  • Fix underlying cause
  • Apologize where appropriate
  • Implement monitoring

Handling harmful content

When AI generates problematic outputs:

Immediate actions:

  • Add filters for identified content
  • Review recent outputs
  • Alert content moderation
  • Consider feature disable

Investigation:

  • How was safety bypassed?
  • What prompts triggered it?
  • Are there systematic gaps?
  • What was the exposure?

Remediation:

  • Update content filters
  • Improve model guardrails
  • Notify affected users
  • Update safety testing

Incident response team

Core roles

Incident Commander:

  • Owns overall response
  • Makes key decisions
  • Coordinates responders
  • Manages communications

Technical Lead:

  • Drives technical investigation
  • Coordinates engineering response
  • Validates fixes

Communications Lead:

  • Manages stakeholder communications
  • Drafts public statements
  • Coordinates with PR/legal

AI-specific roles

Consider including:

  • AI/ML expert (model behavior)
  • Ethics/bias specialist
  • Domain expert (affected area)
  • Legal/compliance (regulatory issues)

Documentation

During incident

Track in real-time:

  • Timeline of events
  • Actions taken
  • Decisions made
  • Who was involved
  • Communications sent

Post-incident report

Document:

  • Incident summary
  • Timeline
  • Root cause
  • Impact assessment
  • Response evaluation
  • Lessons learned
  • Action items

Common mistakes

Mistake Problem Solution
No AI-specific procedures Missing AI failure modes Adapt procedures for AI
Slow detection Damage accumulates Comprehensive monitoring
Technical-only response Missing harm mitigation Include ethics/communications
Skip post-mortem Same incidents recur Always conduct review
Blame individuals Systemic issues persist Focus on process improvement

What's next

Build robust AI operations: