AI Incident Response Framework

Traditional incident response assumes deterministic software. When a web server returns 500 errors, the runbook is clear: check logs, identify the failing component, fix or rollback, verify. AI incidents are different. The system is "working" — it is returning 200 OK — but the outputs are wrong, harmful, or leaked data they should not contain.

You need an AI-specific incident response framework.

AI Incident Categories

Hallucination Spike — The model starts generating factually incorrect outputs at a higher rate than baseline. This can be caused by model updates, context retrieval failures, or prompt template changes.

Data Leakage — The model outputs information it should not have access to — other users' data, internal system prompts, training data fragments, or PII from the context window.

Quality Degradation — Output quality drops gradually over time due to model drift, changes in input distribution, or degradation of supporting systems (embeddings, retrieval, caching).

Adversarial Exploitation — An attacker successfully manipulates the model through prompt injection, jailbreaking, or other adversarial techniques.

Cost Explosion — A bug or traffic pattern causes inference costs to spike dramatically. This is a business incident with technical causes.

The Response Process

Detection — Automated monitoring catches the anomaly through quality metrics, security alerts, cost alerts, or user reports. The faster you detect, the smaller the blast radius.

Classification — Severity assessment based on impact (how many users affected), sensitivity (is data being leaked), and urgency (is the problem getting worse). Severity determines the response team and communication cadence.

Containment — Immediate actions to limit damage. This might mean switching to a fallback model, disabling a feature, activating a circuit breaker, or rolling back a recent change. Containment first, root cause second.

Root Cause Analysis — Once contained, determine what caused the incident. Was it a model update, a data pipeline failure, an adversarial attack, or a configuration change?

Remediation and Review — Fix the root cause, update monitoring to catch similar incidents earlier, and document the incident for the team. Every AI incident should make the system more resilient.