AI security testing must address both traditional IT vulnerabilities and AI-specific threats. According to the AAIA™ framework, " Vulnerability Assessments " are essential for identifying weaknesses in the AI pipeline, including insecure APIs, lack of input sanitization, and susceptibility to adversarial attacks. This assessment helps auditors determine if the system can resist exploitation, such as prompt injection or data poisoning. While regression testing (Option A) ensures consistency and data validation (Option D) ensures quality, a dedicated vulnerability assessment is the only method focused specifically on the " Security " posture of the model and its supporting infrastructure.