The structure behind safe AI
An AI governance framework you can actually implement
Most AI governance guidance is written for regulators and reads like it. This page explains the subject in plain terms: what a framework contains, how ISO 42001, the NIST AI RMF and the EU AI Act relate to each other, and how to put governance in place. The framework itself is a free download, sized for UK organisations that want working controls rather than shelfware.
Start with the definition
What is AI governance?
AI governance is the set of policies, roles, processes and controls an organisation uses to make sure its use of artificial intelligence is safe, lawful and aligned with its goals. It covers knowing what AI is in use, who is accountable for it, how risks are assessed, and how systems are monitored over time.
An AI governance framework is that definition made concrete: the document that names the owners, sets the rules, defines the risk method and fixes the review dates. You will also hear the phrase responsible AI, which expresses the same ambitions in ethical language; a framework is how those ambitions become assignments, controls and evidence.
Crucially, a framework is not a treatise. The useful ones are short enough that a new starter reads them in one sitting and a board can challenge them in one meeting.
The complete framework, free to download
Every pillar on this page as an adoptable document: pre-written sections, owner prompts per pillar and a first-quarter rollout plan. No email address, no registration wall.
The moving parts
What an AI governance framework covers
Six pillars. Every credible framework contains a version of these, whatever it calls them; the downloadable document gives you each as a section to adopt and adapt.
AI inventory and ownership
A register of every AI system in use: the tools you bought, the assistants embedded in software you already own, the models you build, and the tools staff adopted without asking. Each entry gets a named owner, because unowned systems are ungoverned systems.
Policy and acceptable use
The rules your people follow day to day: which tools are approved, what data may go into them, where a human must review the output, and how exceptions are requested. This is the layer most of your organisation will ever see.
Risk assessment
A repeatable method for weighing each AI use case before it goes live and again when it changes. Without one, approval decisions come down to whoever shouts loudest in the meeting.
Controls through the lifecycle
Approval gates for new use cases, human oversight where outputs carry consequences, data protections in procurement contracts, and a defined route to retire a system that no longer earns its risk.
Monitoring and incidents
Watching for drift, misuse and quiet scope creep, plus a plan for the day an AI system produces something harmful. Organisations rehearse cyber incidents; almost none have rehearsed an AI one.
Review and reporting
A fixed cadence for revisiting the register, the risks and the policy, and a summary the board can act on. Governance that is written once and never revisited decays into fiction within a year.
Two pillars have dedicated guides on this site: the AI governance policy guide walks through the rulebook section by section, and the AI risk assessment method gives you the repeatable process with its own template.
The standards landscape
How ISO 42001, NIST AI RMF and the EU AI Act fit together
These three get treated as competitors. They are not: one is a certifiable management standard, one is a working method, one is law. A sensible framework borrows from all three.
| Reference | Who it comes from | Legal status | Role in your framework |
|---|---|---|---|
| ISO/IEC 42001 | International standard, published December 2023 | Voluntary, independently certifiable | The management system: it defines how an organisation runs AI governance as an ongoing discipline, with auditable evidence. The strongest signal to clients and regulators that your framework is real. |
| NIST AI RMF | US National Institute of Standards and Technology | Voluntary guidance, free to use | The working method: four functions (Govern, Map, Measure, Manage) that give you practical questions for assessing individual AI systems. Slots neatly inside an ISO 42001 management system as the risk engine. |
| EU AI Act | European Union regulation, in force since August 2024 | Law, applying in phases | The legal floor: it bans certain practices outright and attaches obligations by risk category. UK organisations are caught when their AI systems or outputs are used in the EU, so its risk categories belong in your classification step regardless. |
For most UK organisations the practical reading is this: build your framework so its evidence would satisfy an ISO 42001 audit even if you never certify, use the NIST functions as your assessment questions, and classify every use case against the EU AI Act's categories so that nothing high risk arrives as a surprise.
The case for it
Why AI governance matters now
Because the adoption already happened. Staff across your organisation are using AI assistants today, sanctioned or not, and the failure modes are concrete: confidential material pasted into public tools, fluent wrong answers copied into client deliverables, and vendors switching on AI features inside software you already licensed, without a decision from anyone on your side.
The outside world has noticed. Client due diligence and supplier security questionnaires increasingly carry AI governance questions, insurers are starting to ask, and the regulatory direction across the EU and UK points one way. Answering "we have a framework, here is the evidence" is a different conversation from answering "we trust our people".
None of this requires banning anything. Governance done well is what lets you say yes to AI quickly, because the checks are defined and someone is authorised to run them.
From document to practice
How to implement AI governance
Five steps, in dependency order. Each one produces an artefact you keep; none of them requires a transformation programme.
Give it an owner and a mandate
Someone senior must hold AI governance as a named responsibility, with the authority to say no. A committee without an owner produces minutes, not decisions.
Build the inventory
Survey teams, check expense claims and network logs, and list every AI system in use, including the embedded assistants nobody thinks of as AI. This step almost always surprises the leadership team, and the surprises are the point.
Adopt the framework and write the policy
Take a framework (this one downloads below), strip it to what fits your size, and turn the acceptable use pillar into a short policy people can follow. The policy guide covers that document section by section.
Risk-assess the use cases that matter
Run your highest-stakes use cases through a structured assessment first: anything touching client data, regulated decisions or public outputs. The rest can follow on a schedule.
Set the rhythm and keep evidence
Quarterly register reviews, an annual policy refresh, and a record of every decision. Evidence is what turns your framework from a claim into something you can show a client, an auditor or a certification body.
Prefer to start from an assessed baseline? Our readiness assessment runs the inventory and the first risk pass for you, with published fixed prices so you know the cost before the first call.
Quick answers
AI governance framework questions, answered
What is AI governance?
AI governance is the set of policies, roles, processes and controls an organisation uses to make sure its use of artificial intelligence is safe, lawful and aligned with its goals. In practice it means knowing what AI is in use, who is accountable for it, how risks are assessed, and how systems are monitored over time.
Why is AI governance important?
Because AI use is already happening in your organisation, with or without permission, and the exposure lands on you either way: client data pasted into public tools, confident wrong answers acted on, and due diligence questionnaires that now ask directly how you govern AI. A framework converts that unmanaged exposure into owned, assessed and monitored risk.
How do we implement AI governance?
In this order: appoint an owner, inventory every AI system in use, adopt a framework and publish a short policy, risk-assess the highest-stakes use cases, then set a review cadence and keep evidence. The five-step section on this page expands each stage, and the downloadable framework includes a rollout plan.
Is an AI governance framework the same as an AI policy?
No. The framework is the whole structure: inventory, accountability, risk assessment, controls, monitoring and review. The policy is one component of it, the rulebook staff actually read. Most organisations need both, and the policy is usually the first artefact worth shipping.
From framework to evidence
Put a working framework in place
Download the framework and adopt it yourself, or book a scoping call and we will implement it against your actual AI estate, at published fixed prices.