AI Governance Framework
An AI Governance Framework Built for the Way AI Works
Five connected dimensions of control — a common operating layer across every AI initiative, regardless of model, vendor, application or orchestration framework.
Most governance frameworks were designed for software that behaves the same way every time it runs. AI does not: models change, vendors change, and agents take actions nobody approved individually.
Applied across every AI system
The five dimensions
One operating layer, five connected dimensions.
Each dimension answers a different question, and each depends on the ones around it. Together they turn governance from something documented into something operational.
Governance
Set direction and accountability. Establish the organisational rules, ownership and decision structures that determine how AI is introduced, used and managed.
6 controlsSecurity & Control
Make governance enforceable. Convert policy into practical safeguards across applications, models, tools, data and actions.
6 controlsRisk & Cost
Manage the AI trade-offs. Balance risk, performance, quality and economics across the estate.
4 controlsObservability
Know what AI is doing. Turn AI activity into evidence teams can investigate, understand and act on.
5 controlsAssurance & Operations
Make governance operational. Give security, risk, technology and business teams the workflows and evidence required to operate AI responsibly — so compliance becomes a by-product rather than a separate reporting exercise.
5 controlsThe control matrix
What each dimension actually controls.
Twenty-six controls across the five dimensions. Every one has an owner, a policy behind it and evidence coming out of it.
Set direction and accountability
Establish the organisational rules, ownership and decision structures that determine how AI is introduced, used and managed. Without this layer everything below it is optional, because nobody is answerable for it.
Make governance enforceable
Convert policy into practical safeguards across applications, models, tools, data and actions. This is the dimension that closes the gap between what a policy says and what a system actually does.
Manage the AI trade-offs
Balance risk, performance, quality and economics across the estate. Not every system deserves the same scrutiny, and treating them equally wastes effort where it matters least.
Know what AI is doing
Turn AI activity into evidence teams can investigate, understand and act on. This matters more as systems gain autonomy: with agentic AI, the consequence lands before any human review.
Make governance operational
Give security, risk, technology and business teams the workflows and evidence required to operate AI responsibly — so compliance becomes a by-product rather than a separate reporting exercise.
How they connect
Not a checklist. A loop.
Governance defines what should happen, Security & Control enforces it, Risk & Cost decides where to concentrate effort, Observability records what actually happened, and Assurance turns that record into evidence and improvement.
That cycle is described in full on the operating model page, and the usual order of adoption on the maturity path.
Questions
Framework FAQ
What is an AI governance framework?
A structured set of policies, controls and processes covering how AI systems are introduced, secured, monitored and evidenced. It answers what AI exists, what it can reach, what it may do, and whether it stays inside acceptable risk — continuously, not once a year.
How is this different from a policy document?
A policy states intent. A framework operates: it maintains a live inventory, enforces controls at runtime, records what happened, and feeds evidence back into the policy. The five dimensions are what turn a written rule into something that actually constrains behaviour.
Do we need all five dimensions to start?
No. The dimensions are connected but implemented progressively — most organisations begin with Governance (inventory, ownership, policy) and add depth as their estate grows. See the maturity path for the usual sequence.
Does it work with our existing AI vendors and frameworks?
The framework is deliberately model, vendor and orchestration agnostic. It sits as a common operating layer across whatever AI your teams have already adopted, rather than requiring a particular stack.
Who owns the framework internally?
Ownership is usually shared across security, risk and technology, with a named owner per AI system underneath. What matters is that every dimension has someone accountable — an unowned dimension is where governance quietly stops.
How long does it take to implement?
The pace is set by organisational clarity more than tooling. Building an accurate inventory and agreeing ownership is typically the longest part, and it is also the part everything else depends on.
Get Started
Put the framework to work.
See how Govreign turns five connected dimensions into one operating layer across your AI estate.