/ THE SHORT ANSWER
- 01Prepare containment and manual fallback before production.
- 02Preserve enough context to reconstruct the system state.
- 03Turn incidents and near misses into evaluation and control updates.
/ dotSuper point of view
AI incident response must cover the whole socio-technical system. The harmful event may begin in a source, prompt, permission, model, tool, interface, human decision, or missing control.
What the evidence says
OWASP’s LLM risk guidance covers prompt injection, sensitive-information disclosure, supply-chain issues, improper output handling, excessive agency, and unbounded consumption relevant to incident scenarios.
A practical decision framework
It is designed to make the decision inspectable, not to imitate a platform ranking formula, certification checklist, or legal test.
- Detect: monitoring, user report, anomaly, evaluation failure, supplier notice, or cost alert.
- Contain: pause, isolate, revoke, restrict, roll back, switch to manual, and preserve evidence.
- Recover: validate the fix, restore gradually, communicate, and monitor heightened signals.
- Learn: root and contributing causes, evaluation cases, control updates, ownership, and follow-up.
| Step | Decision to record |
|---|---|
| 01 | Detect: monitoring, user report, anomaly, evaluation failure, supplier notice, or cost alert. |
| 02 | Contain: pause, isolate, revoke, restrict, roll back, switch to manual, and preserve evidence. |
| 03 | Recover: validate the fix, restore gradually, communicate, and monitor heightened signals. |
| 04 | Learn: root and contributing causes, evaluation cases, control updates, ownership, and follow-up. |
How to put it into practice
Include business, technical, security, and communications owners.
Define a minimum incident record with system version, model, prompts, sources, retrieved context, tools, permissions, inputs, outputs, actions, reviewer decision, and consequence.
- Name the accountable owner and the decision this work must enable.
- Record the current evidence, assumptions, exclusions, and next review trigger.
- Measure a useful outcome rather than treating publication or deployment as success.
What this page cannot conclude
- 01Incident obligations vary by sector, contract, geography, and affected data or people.
- 02This runbook does not replace cybersecurity, privacy, safety, legal, or regulatory response plans.
- 03Publication, technical eligibility, or good practice cannot guarantee ranking, referral traffic, citation, adoption, or a business outcome.
Sources
- 01AI Risk Management FrameworkNational Institute of Standards and Technology · accessed Aug 30, 2026
- 02Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence ProfileNational Institute of Standards and Technology · accessed Aug 30, 2026
- 03OWASP Top 10 for LLM Applications 2025OWASP GenAI Security Project · accessed Aug 30, 2026
Our editorial standard · Found an error? Send a correction with its source.
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dotSuper Research Desk. (August 30, 2026). The AI Incident Response Runbook: Detect, Contain, Decide, Learn. dotSuper. https://dotsuper.net/feeds/applied-systems/ai-incident-response-runbook