incident response

**Incident Response** Incident response for AI systems requires prepared playbooks, rapid rollback capabilities, and systematic post-incident reviews to handle model failures, unexpected behaviors, and production issues that can severely impact users and business operations. Incident playbooks: pre-defined procedures for common failure modes—model producing harmful content, performance degradation, data pipeline failures, and availability issues. Include escalation paths and communication templates. Quick rollback: maintain ability to revert to previous model version within minutes; feature flags, model versioning, and traffic splitting enable fast rollback. Shadow deployments help validate before full rollout. Detection and monitoring: alerting on key metrics (latency, error rates, safety classifier triggers, user feedback signals); catch issues before widespread impact. Incident classification: severity levels (P0-P3) determining response urgency and escalation; clear ownership for each level. Immediate response: contain the issue (circuit breakers, traffic reduction), communicate to stakeholders, and begin investigation. Post-incident review (postmortem): blameless analysis of what happened, why, and how to prevent recurrence; document timeline, root cause, and action items. Runbook updates: incorporate learnings into procedures. AI incidents can have unique characteristics (gradual degradation, subtle behavior changes) requiring specialized monitoring and response practices.