AI Security Testing Methodology defines a structured process for evaluating the security, robustness, privacy, and reliability of artificial intelligence systems. Unlike traditional software security testing, AI security testing must evaluate both software infrastructure and machine learning behavior. This includes prompt manipulation, model abuse, data poisoning, adversarial attacks, and agent exploitation. The methodology integrates classical penetration testing, adversarial machine learning research, and AI-specific security frameworks.