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Connect rules to working evidence
AI governance framework
An AI governance framework connects the decisions an organisation must make about AI with accountable owners, controls and evidence. This guide offers a practical working structure for adapting existing governance to a defined set of systems. It is different from a policy template or a consulting engagement, and it is not a legal certification. Start with the actual uses of AI and make the required decisions and records visible.
Use a practical framework outline to identify missing owners and evidence before writing more policy.
NIST describes its AI risk framework as voluntary guidance for design, use and evaluation. NIST: AI Risk Management Framework ↗
NCSC guidance covers design, development, deployment and operation. NCSC: secure AI system development ↗
New to the topic? Read the background on artificial intelligence.
Connect rules to working evidence
what should the framework cover first?
Identify the systems, intended purposes and people accountable for them.
Keep an inventory that records the workflow, business owner, technical owner, source dependencies and supplier. The voluntary NIST AI Risk Management Framework organises risk work through Govern, Map, Measure and Manage. Those functions provide a reference for discussion, not a certificate that a system is safe. AI governance consulting helps implement the decisions and controls when the organisation needs practical delivery support.
Record actual systems.
Name accountable owners.
Describe intended use.
Connect rules to working evidence
which decisions need recorded authority?
Define who approves use, access, changes, exceptions and withdrawal.
An illustrative organisation may allow staff to draft summaries but require approval before a workflow sends messages or changes records. Write those distinctions into the decision map. AI operating model consulting addresses the recurring roles and handoffs behind it. A committee name is not a decision rule unless the responsible people know which evidence they receive, what they can approve and when an issue must be escalated elsewhere.
Connect rules to working evidence
what evidence should support those decisions?
Link risk records and evaluation results to the specific approval they inform.
Use an AI risk assessment to describe possible failures, affected people and proposed controls. Use LLM evaluation to test representative behaviour. For a document assistant, a control might require source-linked answers and refusal when the material is insufficient. Keep the observed result with the responsible reviewer rather than treating a written control as proof it works. Document unresolved limits and the owner accepting or investigating them.
| Check | What to establish |
|---|---|
| 1 | Link risks and controls. |
| 2 | Preserve test evidence. |
| 3 | Record unresolved limits. |
Connect rules to working evidence
how should policy and suppliers fit into the framework?
Carry the same use boundaries into staff guidance and buying requirements.
The AI policy template helps structure permitted-use rules. AI consulting RFP support turns system requirements into supplier evidence questions. Confirm who owns supplier review, configuration changes and exit arrangements. Applicable legal and professional duties require the appropriate specialist interpretation; a generic framework cannot determine those for every context. Public-sector examples can inform design without becoming universal obligations for private organisations.
Connect rules to working evidence
how does the framework stay useful?
Review incidents and material changes against the same ownership and evidence structure.
Agree which changes trigger reassessment: a different data source, new users, a new action or altered supplier behaviour. AI operations consulting supports recurring review, while AI governance training helps staff understand their part. Keep the framework small enough to apply to real work and record where it fails. More documents do not establish stronger governance if decisions still happen without the necessary people or evidence.
Define reassessment triggers.
Review real exceptions.
Keep records maintainable.
Make the decision concrete
What does the current task cost in time?
Choose a task connected to ai governance framework and enter its current workload. This gives you a baseline to discuss before buying training or commissioning a build.
A baseline is useful even if you decide to keep the process manual. Measure the work that matters, then compare quality and review effort as well as time.
Assess the workflow before changing it, or plan business AI training if the gap is how people use existing tools.
Your current workload
Estimate how much time the existing task takes before discussing ai governance framework.
16 hours per week
People × minutes × weekly tasks ÷ 60. A workload baseline from your inputs, not a prediction of AI time savings. Include review and support time when comparing a proposed workflow.
A useful AI investment leaves your team able to do the work and judge the result.
Continue with the right question
Where should you go next?
Use the overview for context, then follow the pages that match your delivery decision.
See our ai consultancy service or read the ai implementation consulting overview.
FAQ
Questions, answered
Evidence and further reading
Which sources support the guidance?
Source links support the specific background described here. The practical checklists and illustrative examples are predeploy’s guidance.
NIST describes its AI risk framework as voluntary guidance for design, use and evaluation. NIST: AI Risk Management Framework ↗
NCSC guidance covers design, development, deployment and operation. NCSC: secure AI system development ↗
Background reading on the broader field of artificial intelligence. Wikipedia: artificial intelligence ↗
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