How do we create an AI governance policy for employees?

What should an employee AI governance policy achieve?

An employee AI governance policy should turn management decisions into practical rules people can follow during ordinary work. It should explain which tools are approved, what information employees may use, which outputs require review and how to ask for a new use case. The aim is consistent decisions that support useful work while protecting business information and the people affected by it.

A policy becomes useful when an employee can answer a real question with it. Can a sales colleague upload a customer contract to generate a summary? Can a manager use an assistant to compare job applications? Can an agent update a financial record without approval? Broad statements about using AI responsibly do not resolve these decisions.

This guide offers a framework for UK businesses to adapt. The policy needs to fit the organisation’s activities, contracts and risk profile. A short employee document can sit alongside more detailed approval records, security standards and privacy assessments rather than trying to reproduce every technical control in one place.

Who should own the policy and approve exceptions?

Name an accountable business owner with enough authority to resolve competing priorities. Bring together the people responsible for IT, security, privacy, HR and the processes using AI. In a smaller business, one person may cover several responsibilities. The important point is that decisions have clear owners and do not disappear between departments.

Define who can approve a tool, a new information source and a higher-risk workflow. These are separate decisions. A tool approved for public marketing drafts has not automatically been approved to process employee health information. Record approvals with the purpose, permitted users, data boundaries, reviewer and review date.

Exceptions need an owner, an expiry date and a documented reason. A senior employee should follow the same route as everyone else. AI consultancy can help structure the approval process, while management retains responsibility for accepting business risk. Avoid a policy that delegates every practical question to an unnamed committee.

Which people, tools and activities should the policy cover?

Define the scope in ordinary language. Cover employees, contractors and other authorised users handling organisational work, whether the AI capability appears in a standalone chatbot, office software, a browser extension, a meeting assistant or a connected agent. People may not recognise an embedded feature as an AI tool unless the policy gives examples.

Include the use of business information on personal devices and personal accounts where applicable. Explain which related policies continue to apply, such as acceptable use, information classification, confidentiality and procurement. The AI policy should connect these rules rather than create contradictory alternatives.

State what the policy does not authorise. Employees should not interpret access to a feature as approval to use it for every purpose. A clear scope helps distinguish a permitted internal drafting task from a new automated decision or a customer-facing service that requires separate assessment.

How should you classify allowed, restricted and prohibited uses?

Use a few understandable categories with examples. Routine approved use might include drafting a public event announcement from authorised facts. Restricted use could include summarising confidential business documents in a configured business tool after approval. Prohibited use might include entering passwords, bypassing access controls or impersonating another person.

Classify the use case rather than the product alone. A single assistant can support both a low-impact writing task and a consequential decision affecting an individual. Assess the nature of the information, the consequence of an error, the audience and the authority granted to the tool.

Give employees a route for ambiguous cases. The policy can instruct them to pause and ask the named owner when an activity does not clearly fit an approved example. Make that route responsive enough to work in practice. A prohibition with no workable alternative often pushes experimentation outside the organisation’s view.

What information can employees put into an AI tool?

Create concrete data rules covering prompts, uploaded files, screenshots, meeting recordings, connected sources and feedback. A person may avoid typing a customer name while uploading a document containing hundreds of customer records. The policy should treat every input route consistently.

Use the existing classification scheme and illustrate it with familiar information. Public material may be acceptable in an approved workflow; confidential commercial information requires defined controls; credentials should never be supplied as prompt content. Personal information needs assessment of the purpose, permissions and applicable requirements rather than a blanket assumption that a subscription makes it suitable.

Explain that deleting a name does not necessarily anonymise a record. Details about a role, location or unusual event may still identify someone. When employees do not know whether material is safe to use, provide an approved synthetic example or another method of completing the task.

What should UK businesses consider about personal information?

The ICO’s guidance on AI and data protection addresses accountability, transparency, lawfulness, accuracy and other data protection principles. Its current page notes that the guidance is under review following legislative changes. Check the current position when designing a live use case rather than copying an old policy template and assuming it remains sufficient.

Operationally, involve the privacy owner early when a proposed workflow uses personal information. Document what information is needed, why it is needed, who receives it and how long it remains available. Consider the assessment, transparency and individual-rights arrangements appropriate to the activity. Obtain specialist advice where the consequences or legal position need it.

A general employee policy cannot by itself approve every form of processing. Keep detailed assessments with the specific workflow and tell employees which uses have been authorised. Sensitive employment, health or eligibility decisions deserve a separate review before experimentation reaches live records.

How should approved tools and accounts be managed?

Maintain an approved-tool register with the product, account type, owner, permitted use and relevant configuration. Include any restrictions on connectors, external sharing and optional features. Approval of a business account should not be read as approval of a personal account carrying the same brand name.

Technical settings should support the written rules. Review authentication, account lifecycle, permissions and what happens when someone leaves the business. Microsoft 365 support can help align relevant tenant settings and access with the intended policy. Confirm the actual configuration rather than relying on assumptions about defaults.

Give employees an easy way to request a tool before creating an account or installing an extension. Procurement should consider contract terms, security information, data handling and ongoing costs. A free trial can still create information exposure or an unmanaged dependency. Record when approval expires or must be reconsidered.

What review should employees perform before using an output?

Specify review by task and consequence. For a draft email, check accuracy, tone, confidentiality and commitments. For a financial explanation, reconcile numbers to the approved report. For code or a workflow change, use the organisation’s normal technical review and deployment controls. A fluent answer should never substitute for evidence.

The reviewer needs relevant knowledge, access to the original information and enough time to challenge the result. Define what happens when the tool invents a source or expresses certainty where the evidence is incomplete. Employees should be able to reject an output and use the established process without pressure to approve it.

Keep a human owner for the final business outcome. The policy should distinguish assistance from authority: generating a proposal does not approve spending, change a contract or authorise a customer commitment. Where professional judgement is required, route the output to the appropriately qualified person.

What should the rules say about agents and automated actions?

An agent that changes records or sends communications needs tighter boundaries than an assistant that creates an editable draft. Specify the systems it may access, actions it may take, financial or operational limits and approvals required. Grant the smallest set of permissions that supports the agreed task.

Test failures as well as successful demonstrations. What happens if an action is requested twice, an input is incomplete or a connected system is unavailable? Identify how staff can inspect activity, stop further actions and recover from an incorrect change. Approval should cover the operating workflow, not just a promising prototype.

Use Power Platform consulting where a structured workflow needs defined approval stages, and system integration support where information moves between applications. The policy should require reassessment when an agent gains a new connector or broader authority.

How should knowledge sources and document permissions be controlled?

An assistant connected to internal documents inherits a practical dependency on the quality and accessibility of those documents. Identify approved sources, document owners and review dates. Conflicting or obsolete procedures can produce unreliable answers even when the tool itself is functioning as designed.

Review access before connecting a knowledge source. Some files may be available more widely than their owners realise. SharePoint consultancy can support document organisation and permission review, but the business must still decide which information each role should be able to use.

Include a route for reporting incorrect or outdated source material. The employee should not have to silently work around a flawed knowledge base. Keep the source correction and any resulting AI configuration change traceable so that a known issue does not reappear in the next version of the workflow.

What training and employee acknowledgement should you require?

Provide short, role-specific training before employees start an approved workflow. Teach the practical decisions the policy expects: choosing an authorised account, recognising restricted information, checking an output and reporting a concern. A demonstration of impressive prompts is not enough.

Use realistic scenarios. Ask whether a customer spreadsheet can be uploaded, whether an invented reference is acceptable and what an employee should do when a manager requests an unapproved use. Include accessible written guidance and time for questions. Record completion and refresh the training when material rules change.

An acknowledgement shows that an employee received the policy, but it does not prove they can apply it. Managers should model the same behaviour and make room for review in workloads. Digital transformation planning can help connect these responsibilities to the wider change programme.

How should mistakes, data exposure and security concerns be reported?

Give employees a simple reporting route and examples of what to report. These may include confidential information entered into an unapproved service, an unexpected automated action, suspicious instructions in a retrieved document or a repeated quality failure. Ask for the tool, activity and time without encouraging further circulation of sensitive content.

Use existing incident procedures to assess containment and next steps. The responsible team should decide whether access needs to be suspended, evidence preserved or other parties involved. The employee policy should not expect an individual user to interpret every possible reporting obligation during an incident.

The NCSC’s secure AI guidelines place security across design, deployment and operation. Apply that lifecycle approach to connected workflows. Managed security support can help connect AI-related concerns with the organisation’s established response arrangements.

How can you monitor compliance without creating unclear surveillance?

Define the monitoring purpose and use proportionate evidence. For an approved workflow, useful information may include access records, changes to permissions, failed actions and reported exceptions. Decide who can see those records and how long they are needed. Prompts and outputs may themselves contain sensitive information.

Explain the monitoring arrangement to employees through the appropriate channels and involve the relevant privacy and HR owners. Avoid collecting all conversations simply because a product makes it possible. The organisation needs a reasoned approach that fits its responsibilities and the workflow being assessed.

Combine technical checks with conversations and sample reviews. A low incident count may mean the process works well or that people do not know how to report. A cyber security audit can help assess whether access and control arrangements match the written policy rather than assuming the document alone proves compliance.

What approval record should you keep for each AI workflow?

Keep a simple register alongside the employee policy. Each entry should identify the business problem, accountable owner, users, tool and account configuration, information sources and intended output. Include the date it was assessed and the person authorised to approve it. This gives employees and reviewers a shared record of what permission actually covers.

Record the most plausible failure scenarios in practical terms. An assistant could use an obsolete procedure, expose a confidential attachment or propose an action outside its remit. For each scenario, state the control, the owner and how the control will be checked. A list of risks without an operating response does little to help the team.

Include a representative test set and the agreed acceptance criteria. For a customer-summary workflow, examples might cover a straightforward request, conflicting notes, missing information and an instruction to reveal another customer’s details. Document the expected response and the results. Reuse these examples when a material change is proposed, adding cases when new problems appear.

Separate launch approval from continued operation. A pilot may be authorised for a limited group and period while broader use remains subject to review. Record outstanding actions and conditions explicitly. If an approval depends on restricting a connector or completing training, confirm that condition has been met before the workflow is made available.

Finally, assign a withdrawal process. If the owner leaves, the supplier changes a relevant feature or the workflow stops delivering value, someone should decide whether to replace, revise or retire it. Remove unnecessary access and preserve the records the business still needs. Governance includes ending unsuitable uses as well as approving new ones.

What could a practical employee policy contain?

A concise policy could begin with purpose, scope and named ownership, followed by an approved-tools link and permitted-use examples. It should then explain information rules, output checking, limits on automated actions, reporting and the request process for new activities. Put detailed technical requirements in supporting standards that have their own owners.

One illustrative clause is: “Use only approved organisational accounts for approved tasks. Do not enter restricted information or connect a new source unless the activity has been authorised. Check the output against the original information before relying on it, and report mistakes or suspected exposure through the service desk.” Adapt this wording to actual classifications and reporting routes.

Add a short decision checklist employees can remember: Is this tool approved for this task? Is this information permitted? Can I verify the result? Does any action need approval? Who do I contact if something goes wrong? The answers should point to concrete records and people rather than another layer of vague principles.

How should you launch, review and improve the policy?

Test the draft with a small cross-section of employees before launch. Ask them to work through realistic situations and note where the wording leaves them uncertain. Revise those sections, confirm supporting settings and publish the policy somewhere easy to find. Line managers need a briefing so they give consistent answers.

Set a review date and change triggers, such as a new tool, broader data access, an agent taking actions or a significant incident. Keep a version history and explain material changes to employees. Review exceptions and support requests to identify rules that are unclear or impractical.

NetMonkeys can help a Manchester business connect AI governance with its everyday systems and IT support arrangements. Start with the activities people actually perform and the information they handle. A policy is working when employees can make sound routine decisions and know when to ask for help.