What is an AI policy?
Governance, risk and assurance
An AI policy is an internal set of working rules that tells people how AI may and may not be used at work. It normally covers approved uses, restricted uses, data handling, review requirements, ownership, escalation and how the policy is updated as tools and risks change.
Reviewed by Jackie, Head of Learning & Development, Levellers · Last reviewed 8 June 2026
What this means
An AI policy is the written rulebook for day-to-day AI use. It gives staff clear direction on what is allowed, what needs approval, which tools are approved, what data should stay out of prompts, when review is mandatory and who owns exceptions.
In practice, the policy is one part of wider AI governance. Governance sets the overall operating model. The policy turns that model into usable instructions for managers, teams and suppliers.
A good policy should help people decide whether AI is appropriate for a task, not just whether a tool is technically available.
Why it matters
Most AI mistakes at work are not caused by technical ambition. They come from vague working rules. Staff may paste sensitive material into the wrong tool, rely on AI for a task that still needs human judgement or send external content without a proper check.
An AI policy reduces that ambiguity. It helps standardise behaviour across teams, support training and reduce the risk that each department invents its own rules. It also makes supplier and procurement conversations more practical because the organisation can explain what it expects from tools and providers.
A useful policy also works alongside workflow controls such as an AI guardrail. The policy says what the organisation expects. Guardrails help enforce that expectation in live use.
How it works
A useful AI policy usually covers six areas. First, the purpose: why the organisation is using AI and which principles guide its use. Second, scope: who the policy applies to and which systems, tools or providers are covered.
Third, permitted and restricted uses. This should distinguish between low-risk support tasks, such as drafting or summarising, and higher-risk tasks involving customer communications, personal data, regulated decisions or external publication.
Fourth, data handling rules. These should explain what information may be entered into tools, when anonymisation or minimisation is required and when staff must avoid sharing internal or personal material.
Fifth, review and accountability. The policy should define who reviews outputs, when human-in-the-loop AI is required, how issues are escalated and who owns updates.
Sixth, maintenance. A policy should be accessible to staff, supported by training and reviewed on a clear cadence as tools, risks and regulations develop.
Examples
Marketing drafts: The policy may allow AI to generate first drafts of campaign copy, but require human sign-off before publication and prohibit unsupported factual claims or client-specific confidential information in prompts.
Sales meeting notes: A policy can permit approved note-taking tools for internal use while restricting the upload of sensitive commercial data to unapproved public systems.
HR support: The policy may allow AI to draft interview questions or summarise role requirements, but ban automated candidate scoring without approved review, documented justification and appropriate risk assessment.
Internal search and knowledge use: The policy can state which repositories may feed AI tools and when staff must go back to primary source material before acting on an answer.
Common misunderstandings
An AI policy is not just a banned-tools list. It should guide task suitability, data handling, review and escalation, not only name which tools are blocked or approved.
An AI policy is not the whole governance model. It needs backing from ownership, training, supplier due diligence and monitoring.
A generic policy is rarely enough. If it does not connect to real workflows, staff will interpret it inconsistently.
A policy should not pretend all AI uses are equal. Different tasks need different rules. Drafting an internal note is not the same as processing personal data or sending customer-facing content.
Risks and boundaries
A weak policy often fails in one of two ways. It is either too broad to guide behaviour or too strict to be used in practice. Both lead teams to improvise.
Policy also has limits. A document cannot replace system design, review discipline, logging, issue reporting or supplier checks. If a workflow carries material risk, the policy should point to stronger controls and not pretend the document alone is enough.
If personal data is involved, policy should align with applicable data protection duties, including accountability, proportionality and risk assessment. If the organisation operates in jurisdictions affected by AI-specific regulation, the policy may also need updates as those obligations develop.
What to do next
Draft a short first version around one repeated workflow. Include approved tools, prohibited data, review requirements, ownership and an issue-reporting route. If staff can use it during real work without asking for translation, it is probably at the right level.
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FAQs
Is an AI policy the same as an acceptable use policy?
Not quite. It may include acceptable use rules, but a stronger AI policy also covers task suitability, review, data handling, ownership and escalation.
Should an AI policy ban public AI tools completely?
That depends on the risk profile and the work involved. Some organisations prohibit public tools for sensitive work while still allowing approved internal use cases. The key is to make the rule clear and workable.
Who should own the policy?
Usually a named operational owner working with leadership, compliance, security and the teams using AI in practice. Ownership should be visible, not assumed.
How often should the policy be reviewed?
At minimum on a defined regular cycle and sooner if tools, workflows, incidents or regulatory expectations change.