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AI Threat Modeling Explained
This episode of the ISACA Advanced in AI Security Management (AAISM) exam prep series explores how to think like an attacker before an attacker thinks like you. It covers what AI threat modeling actually is, the questions it answers about adversaries, and the established methods you can apply — including newer ones built specifically for AI. By the end you will be able to anticipate how someone might abuse an AI system, choose controls that defend against more than one threat at once, and build the proactive defenses that stop incidents before they start.
What this episode covers
- What AI threat modeling really is — and why it focuses on kinds of attackers rather than specific names.
- Control overlap — why one control often defends against several threats at once.
- The three things threat modeling does — abstract the system, profile attackers, and catalog threats.
- The adversary questions that turn vague worry into prioritized defense.
- STRIDE, PASTA, and LINDDUN — the mature, risk-driven, and privacy-focused workhorses.
- Trike, VAST, OCTAVE, and MAESTRO — niche methods plus the newer AI-specific option for multi-agent and adversarial ML risk.
- Adapting methods for ethics, privacy, and human rights that traditional models overlook.
- AI assisting threat modeling itself — accelerating the exercise while still needing human judgment.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What is AI threat modeling?
AI threat modeling is a key part of risk analysis that begins by identifying the various actors who might threaten a system, without necessarily pinning down who they are. At its core it does three things: creates a simplified abstraction of the system, profiles attackers including their goals, methods, and skills, and builds a catalog of potential threats.
What questions does threat modeling answer about an adversary?
Threat modeling answers practical questions about each adversary: how often they would encounter a target, how likely they are to be detected, what the target is worth to them, what skills and resources they would need, and how much effort the whole attack demands. The answers help prioritize defenses.
Which threat modeling methods apply best to AI systems?
STRIDE is the most mature and easy to apply but struggles with adversarial machine learning. PASTA ties to risk management and business impact but is laborious. LINDDUN focuses on privacy. Trike integrates risk assessment. VAST is visual, agile, and scalable. OCTAVE centers on critical assets. MAESTRO is newer and built specifically for AI, addressing multi-agent environments, adversarial machine learning, and AI autonomy.
Why must traditional threat modeling be adapted for AI?
Traditional threat modeling rarely considers ethics, privacy, and human rights, so for AI you must deliberately adapt your chosen method to include them. AI itself is also increasingly used to assist the threat-modeling process, with tools that help teams generate risk and threat scenarios from established frameworks.
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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 Threat Modeling Explained.