Adversarial testing involves simulating real-world attacks or malicious inputs against AI models (e.g., adversarial examples, poisoning, evasion) to identify how the system behaves under intentional misuse or hostile conditions. The primary objective is to discover weaknesses and control gaps (D) in the model and its surrounding processes-such as inadequate input validation, insufficient monitoring, or missing safeguards against adversarial inputs.
While results from adversarial testing may inform incident response planning (A), KRI definition (B), or security awareness (C), those are secondary benefits. AAIA's coverage of AI threats and vulnerabilities emphasizes adversarial testing as a control validation and gap-identification mechanism , directly addressing AI-specific risk exposure.
References:
ISACA, AAIA Exam Content Outline - Domain 1 and Domain 2: Threats and Vulnerabilities Specific to AI; Testing Techniques for AI Solutions.
ISACA guidance on adversarial testing and AI security posture assessment.