Security of machine learning systems, including supervised, unsupervised, and reinforcement learning models. Deals with adversarial attacks, model evasion, data poisoning, membership inference, model inversion, model robustness, defenses against training dataset manipulation, and security validation in ML/MLOps pipelines.