AI governance is the set of policies, roles and controls an organisation uses to decide how AI systems are built, approved, monitored and retired. It is how responsible AI principles get enforced.
AI security is protecting AI systems, their models, data and connected tools from attack and misuse. It also covers the new risks that AI adoption brings to the rest of the business.
An audit trail is a chronological, tamper-resistant record of who did what, when and to which data or system. It lets organisations reconstruct events, prove compliance and investigate errors, fraud or security incidents.
Human-in-the-loop is a design approach where people review, correct or approve an AI system's outputs or actions at chosen points. It keeps human judgement in decisions that are risky, ambiguous or hard to reverse.
An AI product manager decides what AI-powered features to build and how to judge them, balancing user needs with model capabilities, data, evaluation and risk. They define success when outputs are probabilistic rather than fixed.
Explainable AI is the set of methods that make a model's outputs understandable to people. It shows which inputs drove a prediction and how far that prediction can be trusted.
Guardrails are checks placed around a language model that inspect what goes in and what comes out, blocking or correcting responses that break rules. They enforce policy in code rather than trusting the prompt.
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