AI Governance
Governing what AI agents do
AI governance for agents is the practice of deciding, enforcing, and evidencing what an AI agent is permitted to do in your enterprise systems. It differs from model governance, which covers how a model was built and how it performs, and from content moderation, which covers what a model says. Once an agent can call tools and change records, the governing question becomes whether a given action is authorized — and that has to be answered before the action executes, not detected afterwards.
Why agents need their own governance layer
Enterprise controls were built for people making decisions. IT service management, SOX Segregation of Duties, and access review cycles all assume a human actor whose behaviour sits well inside their permissions, held there by training, judgement, and the friction of having to decide.
None of that applies to an agent. An agent connected to a system can call every operation that system exposes, immediately, and it will if its reasoning concludes it should. There is no gap between access and behaviour for organisational friction to fill, which is why an agent's exposure equals its reach rather than its role.
The three tracks below cover how that gets addressed: defining the boundary, enforcing it in the path of the action, and proving afterwards that it held.
Foundations
What the terms mean, and how the pieces fit together.
Enforcement
Drawing the boundary, and holding it while the action can still be stopped.
Assurance
Human authority, evidence, and the frameworks that ask for both.
Common questions
What is the difference between AI governance and model governance?
They govern different objects and you need both. Model governance covers the model — how it was trained, evaluated, versioned, and whether it performs acceptably — and is largely a data science and MLOps discipline. Agent governance covers what a system built on that model is permitted to do in your enterprise: which tools it may call, which operations it may perform, and which of those need a human to approve them first. A perfectly governed model can be wired to a tool it should never have been given and take an action nobody authorized, because nothing about model provenance constrains an action surface.
Who owns AI agent governance in an enterprise?
In practice it lands across four groups, which is why it is often nobody's until an incident. Security and GRC own the control requirements and answer to auditors. IT owns the registry of what is running and who owns each agent. The teams building agents own the scoping decisions, because they choose the integrations that create the action surface. Internal audit and the risk function consume the evidence. The workable pattern is one shared policy model that all four read from, rather than each maintaining its own view — which is the thing that breaks when governance is assembled from separate design-time and runtime tools.
Find out what your agents can do — before your auditor does.
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