What is the purpose of AI Threat Modeling?

• A. To identify actors, goals, methods, and required controls to create a clearer picture of end-to-end risk.
• B. To optimize user experience and interface design.
• C. To minimize data collection and storage.
• D. To benchmark performance against competitors.

Answer: (A)

Threat modeling for AI is about mapping who could threaten the system, what they want to achieve, how they might attack, and what controls are needed to mitigate those risks. This creates an end-to-end view of risk across data handling, model training and inference, deployment, and governance. By identifying threat actors, their objectives, potential attack techniques (such as data poisoning, adversarial inputs, prompt injection, or data leakage), and the safeguards required (validation, provenance, access controls, monitoring, encryption, privacy measures, and audits), teams can prioritize mitigations and design secure AI architectures. This risk-focused planning guides secure AI throughout its lifecycle, unlike goals that center on user experience, data minimization in isolation, or performance benchmarking, which don’t address how to anticipate and defend against threats.