| 🏠 Back to Exam Syllabus | 📺 RooCloud on YouTube | 🌐 RooCloud Practice Exams |
AI Incident Response — Respond: Containment, Eradication and Recovery
This episode of the ISACA Advanced in AI Security Management (AAISM) exam prep series covers the Respond phase of the AI Incident Response lifecycle — where the team actually stops the bleeding, removes the threat, and brings a system back safely. Responding to an AI incident is genuinely different, and applying old playbooks blindly can fail. Knowing the AI-specific techniques for each step is what lets you do the real work rather than assume a familiar fix will work.
What this episode covers
- The three-step response model — why contain, eradicate, and recover still apply but are harder for AI.
- Containment — the goal of stopping spread, with safety-of-life always taking priority.
- Attack-specific containment techniques for prompt injection, data poisoning, and adversarial inference.
- Eradication — why removing an AI threat often runs deeper than a software patch.
- Retraining, fine-tuning, and isolation as the levers for fully eradicating an AI threat.
- Recovery — restoring safe operation when some threats cannot be fully eliminated.
- Reactivation governance — tightening controls, adding guardrails, and getting stakeholder sign-off before switching the AI back on.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What are the three steps of AI incident response?
The classic three response steps still apply: containment, eradication, and recovery. They are often far less effective for AI than for ordinary systems because AI is complex and probabilistic, so there is rarely a single clean fix. Each step needs AI-specific techniques tailored to the attack.
How do you contain different AI attacks?
For prompt injection, deploy input and output validation and fixed prompt templates to screen out abusive prompts and shield the model. For data poisoning, revoke access to the datasets and the scripts that preprocess them, cutting off further contamination. For adversarial inference, throttle, validate, and sanitize the legitimate input channels the attacker uses, slowing systematic probing. Anything threatening human life or safety demands immediate action.
Why is eradicating an AI threat so difficult?
In ordinary software you patch a flaw or rotate stolen credentials, but AI fixes run deeper. For prompt injection the model may need retraining or fine-tuning, which can be impractical and costly for large models, so input and output controls remain vital long-term defences. For data poisoning, the poisoned data must be isolated and removed and a clean version of the model retrained, with output sanitization as a short-term stopgap. For adversarial inference, robustness and resilience techniques harden the model against future probing.
How do you recover from an AI incident?
Recovery returns the system to a safe, operational state once the threat is contained and eradicated. The honest reality is that some AI attacks cannot be fully eradicated, so the best outcome is to shrink the attack surface to prevent a relapse, actively monitor for any lingering threat, and keep improving the model’s robustness. That means tightening access controls, adding new guardrails, validating those controls after the incident, and getting sign-off from all relevant stakeholders before the AI is switched back on.
📚 Master the ISACA AAISM Exam!
Ready to test your knowledge? Access chapter-specific Multiple Choice Questions (MCQs) and full-length practice exams for the ISACA AAISM certification at RooCloud.com. Solve the chapter-wise questions to reinforce this lesson before moving to the next episode.
Reference: This article is based on concepts discussed in AI Incident Response — Respond: Containment, Eradication & Recovery.