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AI Incident Response — Prepare: IR Team and Tabletop Exercises
This episode of the ISACA Advanced in AI Security Management (AAISM) exam prep series opens a five-part AI Incident Response sub-series with the Prepare phase — the readiness work that decides how much damage gets done when an AI system goes wrong. It covers why AI needs its own IR approach, the AI-specific incidents you must plan for, how to expand the IR team, and why tabletop exercises must be rehearsed differently.
What this episode covers
- Why AI needs its own IR approach and how established standards like ISO 27035 still apply when tailored.
- The AI-specific incidents to prepare for — adversarial attacks, data poisoning, bias exploitation, model drift, unauthorized access, and automation failures.
- Policies, procedures, and documentation adapted for harmful AI output, training-data leakage, and biased predictions.
- The role of model cards and a clear communication and escalation plan in supporting investigators.
- The expanded AI IR team — data stewards, data engineers and scientists, privacy experts, and AI ethicists alongside traditional responders.
- AI tabletop exercises that rehearse the specific moves of dataset exploration and model input-output analysis.
- How simulations expose gaps in tooling, escalation paths, and underlying IR assumptions before a real incident.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What is the Prepare phase of AI incident response?
Prepare is the readiness phase that puts policies, documentation, the right response team, and rehearsed tabletop exercises in place before an AI incident strikes. The goal of incident response is to identify, contain, and prevent threats while keeping the AI system reliable, secure, and ethical, so the faster you can identify, analyze, respond, and recover, the smaller the impact.
What AI-specific incidents should an IR plan cover?
Six stand out: adversarial attacks that tweak input to fool a model, data poisoning that corrupts the training set, bias exploitation that abuses built-in bias for unfair results, model drift where accuracy slowly decays as the world changes, unauthorized access to manipulate or steal information, and automation failures where AI-driven automation produces wrong or dangerous outcomes.
Who should be on an AI incident response team?
Beyond the usual responders, the AI IR team needs data stewards who know the training dataset intimately, data engineers and scientists who can interpret strange model behavior, privacy experts who understand the personal-data impact and regulations, and AI ethicists responsible for the safe and ethical use of the system. Without these voices, an AI incident is easy to misread.
Why do AI tabletop exercises differ from traditional ones?
The skills differ sharply from ordinary forensics. Investigating a poisoned model, for example, requires access to a copy of the very data lake that was contaminated and close teamwork between responders and the data science team to hunt for tampering. Teams should rehearse AI-specific moves such as exploring a dataset for poisoning and analyzing the model’s inputs and outputs, because practised teams are far more effective when a real incident strikes.
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Reference: This article is based on concepts discussed in AI Incident Response — Prepare: IR Team & Tabletop Exercises.