Machine Learning Security encompasses both the protection of the data used for training machine learning models and the integrity of the models themselves. This involves safeguarding against attacks that can compromise the performance of ML systems, such as adversarial attacks, data poisoning, and model stealing. Security measures may include techniques for model robustness, secure data handling practices, regular monitoring of model performance, and ensuring compliance with relevant regulations.