Governance
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.
Related terms
- Prompt Injection — Prompt injection is a class of attack where adversarial text inside a user query, tool result, or external document overrides the AI system's instructions.
- AI Governance — AI governance is the system of policies, controls, and accountabilities that determines what AI is allowed to do inside an organization, who approves AI deployments, how AI decisions are audited, and how risk is managed.
- AI Risk — AI risk is the set of categorized hazards a deployment introduces — including hallucination, bias, data leakage, prompt injection, regulatory non-compliance, vendor lock-in, and unintended automation of harm.