AI Governance
AI governance is the set of policies, roles and controls that decide how an organisation may build, buy and use AI: who approves a use case, what data a model may see, how incidents are handled, and who is accountable when something goes wrong.
No jargon, no math
In plain words, what is it?
Governance is how the rules actually run, not the document that states them. A working setup has four moving parts. A register: every model and use case listed with an owner, like an asset inventory. An approval path: new uses are proposed, risk-tiered and routed, a low-risk drafting helper gets a fast track, a system touching customer data gets legal, security and a named approver. Guardrails at runtime: the rules enforced where the model runs, what it may retrieve, what it may send, what it must log. An incident path: when something goes wrong, who is paged, what gets rolled back, what gets reported. Governance fails in one predictable way: a policy PDF that nobody enforces at deploy time. The test is simple: pick any AI system in production and ask who approved it, what it may see and what happens when it misbehaves. If the answer is a shrug, governance is a shelf document.
Terms that live nearby.
EU AI Act
The EU AI Act is the European regulation that sorts AI systems into risk levels: unacceptable practices are banned outright, high-risk systems must meet documentation, data-quality, logging and human-oversight requirements before they can be sold or deployed, and the rest face lighter transparency duties.
ReadAI Audit
An AI audit is a structured review of how an organisation actually uses AI: every tool, model and workflow is inventoried, then tested for accuracy, data exposure, cost and compliance, with findings ranked by risk and a remediation plan attached.
ReadGuardrails
Guardrails are the checks and limits wrapped around an AI model so its behaviour stays inside your rules: they screen what goes in, constrain what the model can do, and verify what comes out before it reaches a person or a system.
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