Verification of an AI/ML model’s ability to maintain reliable performance when exposed to data not seen during training, including out-of-distribution inputs, temporal drift, and edge case scenarios. Covers validation techniques for predictive robustness, statistical anomaly management, and controlled performance degradation on evolving datasets.