Learning from Incidents
Systematically analyze AI governance incidents to drive continuous improvement
156
Total Incidents142
Post-Mortems89
Lessons Learned67
Improvements42%
Recurrence ↓12 hr
Avg ResolutionRecent Incidents & Post-Mortem Status
Model Performance Degradation
Root Cause
Undetected data drift in production environment
Impact
15% drop in accuracy affecting 5,000 predictionsPost-Mortem Status
Actions Taken
4 / 5 completedFairness Violation Alert
Root Cause
Training data imbalance not caught in validation
Impact
Demographic parity exceeded threshold by 18%Post-Mortem Status
Actions Taken
6 / 6 completedAPI Rate Limit Breach
Root Cause
Insufficient scaling configuration
Impact
Service degradation for 30 minutesPost-Mortem Status
Actions Taken
3 / 3 completedLessons Learned Repository
Early Drift Detection is Critical
Data drift can occur gradually and go unnoticed without proper monitoring thresholds
Key Takeaways:
• Implement PSI monitoring with threshold of 0.1
• Set up automated alerts for feature distribution changes
• Conduct weekly drift analysis reviews
Fairness Testing Must Be Continuous
Static fairness checks at deployment are insufficient; continuous monitoring is essential
Key Takeaways:
• Run daily fairness metrics on production data
• Implement automated retraining triggers
• Maintain demographic parity < 0.05
Infrastructure Auto-Scaling Best Practices
Predictive scaling based on historical patterns prevents service degradation
Key Takeaways:
• Configure auto-scaling with 20% buffer
• Use predictive scaling for known peak times
• Implement circuit breakers for graceful degradation
Post-Mortem Template Standardization
Consistent post-mortem structure improves learning extraction and pattern recognition
Key Takeaways:
• Use 5-why analysis for root cause
• Document timeline with minute precision
• Include quantitative impact metrics
Incident Pattern Analysis
Incident Trends (6 Months)
Root Cause Distribution
Improvement Actions & Implementation
| Improvement Action | Category | Source Incident | Priority | Owner | Due Date | Status | Impact |
|---|---|---|---|---|---|---|---|
| Implement automated drift detection system | Technical | INC-156 | High |
Lisa Wang | Jan 15, 2025 |
65% |
|
| Deploy fairness monitoring dashboard | Monitoring | INC-155 | Critical |
Sarah Chen | Jan 10, 2025 |
80% |
|
| Update scaling policies and thresholds | Infrastructure | INC-154 | Medium |
Mike Johnson | Jan 20, 2025 |
45% |
|
| Create incident response runbooks | Process | Multiple | High |
Emily Davis | Jan 31, 2025 |
30% |
|
Prevention & Recurrence Metrics
Incident Prevention Rate
78%
Recurrence Rate
12%
Time to Resolution
12 hours
Incident Response Knowledge Base
Popular Articles
Handling Data Drift in Production Models
Emergency Rollback Procedures
Bias Detection and Mitigation Strategies