Your incident response playbook probably assumes the compromised system behaves predictably, that the bug can be patched, and that the evidence lives in a known log. An AI system breaks all three assumptions at once.
Traditional incident response frameworks assume properties AI systems don't have: deterministic behavior, patchable vulnerabilities, and evidence that lives in familiar places like system logs and disk images. An AI model can behave differently given the same input twice, a behavioral "bug" doesn't always have a clear patch that fixes it, and evidence of an incident can be scattered across prompts, retrieved context, and agent decisions that were never logged with the same detail as a traditional system call.
NIST SP 800-61r3, published in April 2025, serves as the foundational incident response framework, with MITRE ATLAS extending it to cover AI-specific threat vectors. The NIST AI RMF contributes its four functions (Govern, Map, Measure, Manage) as the underlying governance and risk management structure — neither replaces the other, they complement each other.
The emerging discipline of AI IR includes responding to incidents that target the AI systems themselves: model poisoning, prompt injection, adversarial attacks on ML pipelines, and training data exfiltration — incident categories a traditional security playbook, designed for malware or network intrusion, simply doesn't account for.
2026 is the year two trajectories cross: the operational maturity of agentic AI on one hand, and a binding regulatory framework (the EU AI Act) coming into force on the other. As LLMs move from pilots to customer-facing applications and autonomous agents, security incidents involving AI systems stopped being hypothetical — and in certain jurisdictions there's now a legal obligation to report them too.
An AI incident response playbook is a repeatable operating system for detecting, containing, and recovering from AI system failures, built by predefining triggers, owners, evidence sources, containment options, recovery checks, and communication channels — before the incident happens, not during it. The difference between a company that contains an AI incident in hours and one that takes weeks almost always comes down to whether that definition work was done ahead of time or is being improvised in real time.
Carlos Montiel is an enterprise AI solutions architect. He implements LLMs, Agents, RAG, and orchestrators for companies across Guatemala and Latin America. Reach out for a consultation.
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