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AI Incident Response — Prepare: Policies, IR Team, and Tabletop Exercises
This episode of the ISACA Advanced in AI Audit (AAIA) exam prep series opens a five-part series on AI incident response and focuses on Phase 1 — Prepare. You’ll see how an established global standard adapts to algorithmic crises, why preparation is the most important phase, and the specific policies, documentation, team roles, and rehearsals organizations need in place before any incident occurs. The discussion equips auditors to judge whether their organization is genuinely ready for an AI-related emergency.
What this episode covers
- Adapting ISO 27035-1 — the five-phase incident response framework and how it translates to AI emergencies.
- Why preparation matters most — the survival case for getting policies and people ready before anything breaks.
- Three AI incident types — output abuse harming society, disclosure of training data, and biased or hallucinated predictions.
- Policies, ethics, and documentation — settling ethical debates in advance and keeping model cards crystal clear.
- Communication and escalation protocols — knowing exactly who to call when a crisis lands.
- Upgrading the IR team — adding data stewards, data engineers and scientists, privacy experts, and an AI ethicist.
- Tabletop exercises — AI-tailored simulations that train teams on threats like data poisoning that traditional tools cannot see.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What are the five phases of the ISO 27035-1 incident response framework?
The five phases are prepare, identify and report, assess, respond, and post-incident review. You prepare by building policies and assembling your team, identify and report by detecting the problem and sounding the alarm, assess by determining severity and impact, respond by containing, eliminating and recovering, and finally conduct a post-incident review to learn lessons. The same five steps can be adapted to handle AI crises.
What three types of AI incidents must an organization prepare for?
The three types are abuse of the system’s output to cause harm to society, such as mass-producing fake threatening letters to manipulate an election; disclosure of confidential or personal data the algorithm absorbed during training; and the system generating predictions that are biased, incorrect, or hallucinated, where the computer confidently makes things up.
What is a model card and why is it important during an incident?
A model card is essentially a nutritional label for software. Instead of listing calories it documents the exact data ingredients and logic the system uses, including its internal architecture and the raw information it studied. During an emergency, flawless model documentation lets investigators interpret exactly how the system makes decisions so they are never flying blind.
Who should be on an AI incident response team?
Alongside normal technology and security staff, an AI incident response team must include data stewards or owners who understand the training datasets, data engineers and scientists who built the model and can interpret anomalous behavior, privacy experts who keep the cleanup within global privacy and data sovereignty laws, and an AI ethicist who serves as the moral compass to keep technical fixes safe and ethical for society.
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Reference: This article is based on concepts discussed in AI Incident Response — Prepare: Policies, IR Team & Tabletops.