AI Security

AI security is the discipline of protecting AI systems from adversarial attacks, data poisoning, model theft, and misuse — and of preventing AI from being used as an attack vector against other systems. It extends traditional cybersecurity to cover the unique attack surface of machine learning models and LLM-based applications.

Key AI security threats include prompt injection, training-data poisoning, model inversion (reconstructing training data from model outputs), and adversarial examples. Defense-in-depth for AI systems combines input validation, output filtering, access controls on training data, and continuous red-teaming.