Data drift is the phenomenon where the statistical distributions of input data for machine learning models change over time compared to the training set, degrading prediction accuracy and reliability. In cybersecurity, data drift detection is critical for ML-based threat detection systems, anomaly detection, behavioral analytics, and AI security models, where unmonitored variations in traffic patterns, logs, or user behavior can generate false positives, false negatives, or render automated defenses ineffective.