What is ML system security and model integrity protection?

ML system security ensures models are protected against tampering, poisoning, and unauthorized access.

ML security includes protecting training data, preventing model poisoning, securing inference APIs, and ensuring model integrity. Techniques include access control, encrypted storage, secure enclaves, adversarial training, and anomaly detection. Model theft prevention and API abuse protection are also key concerns.