Working premise
AI incidents can emerge as quality drift, unsafe behavior, cost spikes, latency changes, data exposure, or tool misuse. Response requires both ordinary service telemetry and AI-specific trace evidence.
Operational context
AI incidents can involve quality, unsafe action, sensitive disclosure, retrieval corruption, evaluator failure, provider behavior, or unexpected cost. The first operational task is to establish scope: which release, model, prompt, route, feature, tenant, and time window are affected.
Containment may require more than rollback. Teams may disable a tool, narrow a route, block a prompt version, restore an index, change a policy, increase human review, or pause a feature. The runbook should identify reversible controls and the evidence each action preserves.
Post-incident learning must update the release system. Confirmed examples should become protected tests, monitors should be revised, and assumptions in routing, evaluation, or access control should be recorded. Otherwise the incident document closes while the failure remains reproducible.
Questions to resolve first
Which signal declares an AI quality or control incident?
Can affected behavior be segmented by release, model, prompt, route, feature, and customer?
Which reversible controls can contain the issue without destroying evidence?
Who owns customer, security, legal, provider, and engineering coordination?
Recommended method
- P1
Define severity using user impact, safety, data sensitivity, scope, and reversibility.
- P2
Preserve affected traces, releases, prompts, models, datasets, and evaluator outputs.
- P3
Contain through rollback, routing, feature controls, or human-review escalation.
- P4
Convert confirmed failures into durable tests and updated monitors.
Common failure modes
The service is available, so a harmful quality or action failure never reaches incident response.
Prompts, traces, or configurations are overwritten before the affected state can be reconstructed.
The narrative is completed but no test, monitor, or release rule prevents recurrence.
Control points
- Keep incident access narrower than routine product analytics.
- Record every containment action and affected release.
- Avoid changing evaluators during diagnosis without preserving the original scores.
- Review whether the detection gap was instrumentation, evaluation, or ownership.
Implementation sequence
- S1
Define incident categories, severity, declaration signals, and role ownership for AI-specific failure modes.
- S2
Prepare queries and controls that segment impact and support rollback, route changes, or feature isolation.
- S3
Preserve versions, traces, reviewer decisions, provider notices, and mitigation timestamps in one record.
- S4
Convert confirmed failures into evaluation cases and verify the new control through a release exercise.
Measures worth reviewing
Track detection, declaration, containment, and recovery separately. Add affected traces or tasks, rollback success, repeat-incident rate, and time from incident confirmation to durable regression coverage.
Primary references
This note is an editorial synthesis of public standards, primary institutional guidance, and common engineering control patterns. It is intended to support engineering design and review. It is not a substitute for legal, security, safety, audit, or other professional advice for a specific system.