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AI Incident Runbook: A Practical Starting Template
Create an AI incident runbook covering detection, triage, ownership, communication, containment, and learning.
Last updated
2026-09-14
What is an AI incident runbook?
An incident runbook is an operational guide for responding to a known class of failure. For an AI service, that may include harmful output, degraded quality, data exposure, unavailable dependencies, cost spikes, or a broken human handoff.
A runbook makes the first decisions easier under pressure. It names the signal, owner, severity, containment step, communication path, and record needed for review.
Build a bounded runbook
Choose one incident type. Define the trigger, initial checks, decision points, owner, containment action, stakeholder message, and post-incident record. Do not invent live service metrics or claim that the runbook has been used in production.
Iteretta's AI Strategy & Operations lab practises monitoring, incident response, cost controls, service decisions, and delivery governance.
The incident response sequence
- 01Detect: what signal shows that the service or output is failing?
- 02Triage: how is severity and scope assessed?
- 03Own: who coordinates the response and decisions?
- 04Contain: what is paused, limited, or routed to a human?
- 05Learn: what evidence supports the review and next change?
Common questions
How is an AI incident different from a normal software incident?
The response may include model quality, data, prompt, supplier, human review, and safety questions alongside normal availability and technical checks. The exact scope depends on the service.
What makes a runbook useful?
It should be specific enough to guide the first response, name decision owners, avoid hidden assumptions, and leave evidence for learning after the incident.
This resource is maintained by Iteretta. It is educational information, not legal, financial, medical, employment, or other professional advice.