The AI incident response playbook: what to do when the model misbehaves
A hallucinating support bot or a leaking RAG pipeline is an incident like any other — except most runbooks never mention it. What a working AI incident process looks like.
Your cloud runbook covers database failovers and DDoS attacks. It almost certainly does not cover the moment your AI support agent starts confidently inventing refund policies, or a retrieval pipeline surfaces one customer's documents to another. These are incidents with real customer and regulatory consequences — and they need a process defined before they happen.
What counts as an AI incident
Four categories cover most cases: harmful or fabricated output reaching users, personal or confidential data leaking through prompts or retrieval, cost runaways from loops or abuse, and silent quality regressions after a model update. Each needs a named owner, a severity scale, and a defined first response.
The first hour: contain, then diagnose
Every AI system in production needs a kill switch that is faster than a redeploy: route traffic to a fallback model, a rule-based responder, or a human queue. Containment buys you time to diagnose without the damage compounding. Practice it — a kill switch nobody has tested is a hope, not a control.
Diagnosis needs the right telemetry
You cannot debug what you did not log. Capture the model version, prompt template, retrieved context, and output for sampled traffic — with privacy controls appropriate to the data. When a model provider ships an update and quality shifts overnight, these logs are the difference between a two-hour fix and a two-week mystery.
Communication and follow-up
Treat user-facing AI failures like any other incident: honest status updates, a postmortem focused on systems rather than blame, and a concrete prevention action — an evaluation suite, a guardrail, a monitoring alert — tracked to completion.
Under the EU AI Act and GDPR, some AI incidents are reportable events with deadlines. Knowing your obligations in advance is part of the playbook.
bluebill gives teams the monitoring and audit trail this depends on: model versions, prompts, costs and quality metrics in one place, so an incident is a managed event instead of a scramble.