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Write an AI use policy as a short, practical decision guide: tell employees which tools and tasks are approved, what information they must keep out of AI systems, when a person must check an output, and how to ask for help or report a problem. Assign an owner, give concrete examples, and make the rules work alongside your existing privacy, security, records, intellectual-property, accessibility, and conduct policies.
A policy that simply says “use AI responsibly” leaves employees guessing. The approach below turns broad principles into rules employees can apply before, during, and after using an AI tool. It is a general framework, not a complete compliance policy: obligations depend on the organization, the work, the data, and the jurisdictions involved.
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What should an AI use policy include?
An effective policy answers six questions an employee can act on: Is this tool approved? Is this use permitted? What information can I enter? Who must check the result? When must I disclose or record AI assistance? What should I do if something goes wrong? Build the policy around those decisions, then assign clear ownership and review responsibilities.
Define “AI” broadly enough to include generative AI, automated decision systems, and AI features embedded in other products. Identify the employees, contractors, systems, data, and work products covered. Name the policy owner, the people authorized to approve tools or exceptions, and the teams responsible for privacy, security, legal, and operational review.
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Keep the rules consistent with existing obligations. An AI policy does not replace privacy, information-security, records-retention, intellectual-property, anti-discrimination, accessibility, or professional-conduct rules. State that those rules continue to apply when an AI system is involved.
The National Institute of Standards and Technology (NIST) AI Risk Management Framework describes organizational policies and practices for mapping, measuring, and managing AI risks. NIST released the framework’s version 1.0 on January 26, 2023, and its Generative AI Profile on July 26, 2024. The framework is voluntary; organizations should map it to their actual legal and contractual duties rather than treat it as a substitute for them.
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How should we decide which AI tools and uses are approved?
Approve a tool for a defined purpose and data boundary, not as a blanket endorsement of everything it can do. Keep a current register employees can check before they start, and make the approval process straightforward enough that people have a reason to use it.
Maintain an approved-tool register
For each approved system, record its business owner, approved purposes, permitted data classes, relevant retention settings, and review status. Include third-party systems and AI features in existing products, not just standalone chat or image tools. NIST’s governance guidance calls for an AI-system inventory and policies that address third-party systems.
Give employees a clear route to request a tool that is missing from the register. The owner should coordinate the appropriate security, privacy, legal, procurement, and business reviews before approval. Do not invite employees to test an unapproved tool with real company or customer information while a request is pending.
Describe permitted work in examples
Depending on the organization’s approved tools and data rules, lower-risk examples might include brainstorming, formatting, summarizing non-confidential material, translation, or asking for an explanation of code. Say whether each example is allowed, restricted, or requires prior approval in your organization. An example is not permission to use any tool or to include any data.
Separate experimentation from production use. A tool that may help an employee explore an idea is not automatically suitable for customer-facing work, operational decisions, or integration into a business process. Require approval before a pilot becomes a production use, and name who grants it.
Choose a policy posture deliberately
Organizations can choose a more permissive, controlled, or prohibition-heavy approach, but each has trade-offs. A permissive policy may make useful tools easier to adopt while increasing the need for data controls and monitoring. A restrictive policy can reduce some exposure but may push use out of sight. A controlled approach depends on maintaining the tool register, providing alternatives, and enforcing consistent review.
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| Policy choice | What to weigh |
|---|---|
| Permissive | Employee usefulness and adoption against confidentiality risk, human-review burden, legal or discrimination exposure, and auditability. |
| Controlled | Whether the organization can sustain tool inventory, vendor controls, training, review, and records as uses change. |
| Prohibition-heavy | Whether the restrictions are workable for employees and whether they will reduce risk or drive unapproved use elsewhere. |
These are decision factors, not a promise that any one posture is safest. Choose in light of your organization’s risk appetite, obligations, capabilities, and actual use cases.
Can employees paste company data into an AI tool?
As a default, no: prohibit entering personal, health, confidential, proprietary, regulated, security-sensitive, or otherwise restricted information into public or unauthorized AI systems. Permit an exception only when an approved secure process explicitly allows that data and use.
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The Centers for Medicare & Medicaid Services (CMS) responsible-use guidance and the Internal Revenue Service’s GenAI policy set operational expectations for handling restricted information in their respective contexts. Those agency rules are not automatically the rules for a private company, but they illustrate why a policy should define forbidden inputs rather than relying on vague cautions. The IRS section cited here is dated February 10, 2026.
Organizations should also explain that tool approval is not permission to share every kind of data with that tool. The register should specify which data classes, if any, are permitted for each approved purpose. Employees must follow the narrower rule when a contract, law, security control, or internal data classification imposes a stricter limit.
When must a person review an AI output?
A person should check an AI output before relying on it, publishing it, or using it to affect someone. Require stronger review and documented approval when the output could affect employment, legal rights, safety, finances, access to services, or another consequential interest.
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Use a review checklist
- Accuracy: Are material facts, calculations, quotations, and references correct? Check important claims against reliable sources instead of trusting generated citations.
- Suitability: Does the answer fit the actual audience, context, and decision? Has the employee checked that the tool was approved for this task?
- Privacy and confidentiality: Does the output reveal personal or restricted information, including information that should not have been entered in the first place?
- Fairness and rights: Could the result discriminate, disadvantage a protected or vulnerable group, or affect an individual without a meaningful opportunity for review?
- Accessibility: Can the intended audience access and understand the content, and have relevant accessibility requirements been met?
- Intellectual property: Is the proposed use appropriate under the organization’s copyright, licensing, and attribution rules?
- Security: Does the output include unsafe instructions, code, links, or other material that needs technical review?
For consequential decisions, do not let an opaque AI result become the unreviewed decision about a person. Identify who is responsible for the final decision, require that person to have enough information and authority to challenge or override the output, and provide an escalation path when the result is uncertain or disputed. The Information Commissioner’s Office (ICO) guidance emphasizes meaningful human oversight and the ability to override. It also advises senior management to review and sign off on intended uses in line with the organization’s risk appetite.
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For high-impact uses, record the reviewer’s decision and the basis for relying on the output. Make clear that a nominal human sign-off is not enough if the reviewer cannot understand the relevant context, investigate concerns, or change the outcome.
Do employees have to disclose AI-generated work?
Set disclosure rules by audience, purpose, and risk; do not rely on a blanket rule that either all AI assistance must be disclosed or none of it does. Specify when employees must tell colleagues, customers, regulators, or other stakeholders that AI assisted the work, and what detail is appropriate.
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For example, an internal formatting aid may not need the same notice as AI-assisted material presented as expert analysis or used in a regulated process. The policy owner should define the organization’s disclosure expectations for each relevant work type, and route any legal, regulatory, or contractual disclosure question to the responsible team. Do not let employees imply that work was produced or verified in a way it was not.
When disclosure is required, say what it should communicate: that AI was used, the purpose of that use, material limitations or uncertainty, and the safeguards applied. CMS responsible-use guidance calls for communicating AI use, purpose, limitations, safeguards, and uncertainty. CMS also states that users are fully responsible for the impact and accuracy of content produced with generative AI tools at the agency.
What should employees record, and how should they report a problem?
Set a proportionate recordkeeping rule. For uses that require formal review or approval, retain enough information to reconstruct who used which system, when and for what purpose, what material inputs and outputs mattered, who reviewed the result, what approvals were granted, and whether an incident occurred.
Do not require employees to save every low-risk prompt unless there is a business, legal, contractual, or audit reason to do so. Define where required records belong, who may access them, and how long they are kept under the organization’s retention rules. Recording sensitive prompts in an unsecured location could create a second exposure, so the recordkeeping process must follow the same data protections as other business records.
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How can we prevent shadow AI without making the policy unusable?
Make compliant use easier than concealed use: publish an accessible approved-tool list, explain how to request a new tool, offer safe alternatives for common tasks, and answer questions quickly. A rule with no workable path for everyday needs can encourage employees to use personal accounts or unreviewed features outside organizational controls.
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Pair that access with clear boundaries and consistent accountability. Tell employees not to bypass security controls, conceal AI use where disclosure is required, or use a personal or unauthorized account for company work. Managers should be able to identify the right contact for an exception rather than improvising approvals. Use reporting to find gaps in the approved options and improve the register, not only to identify violations.
Track whether employees can find the policy, understand the data rules, and locate a suitable approved tool. If recurring tasks have no approved option, evaluate whether a controlled solution is feasible; do not assume that stronger wording alone will eliminate unapproved use.
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Provide role-based AI literacy and risk training. A general introduction can explain the policy’s core rules; people who approve systems, handle sensitive data, build AI-enabled workflows, or review high-impact outputs need training matched to those responsibilities.
Training should use realistic scenarios: a prompt containing customer details, an answer with a convincing but false source, a proposed employment decision, an AI feature newly enabled in an existing product, and a disclosure or incident question. Ask employees to practice the policy’s decision steps and know where to get help. NIST governance guidance calls for personnel training and periodic review.
Explain consequences for violations and who can approve exceptions. Apply accountability consistently, distinguishing intentional circumvention from a good-faith report or an honest mistake. Employees need to know that reporting a suspected leak promptly is expected, not a reason to hide it.
Article 4 of the EU AI Act requires providers and deployers of AI systems to take measures to ensure, to their best extent, a sufficient level of AI literacy among staff and other people operating or using systems on their behalf. Whether and how that duty applies depends on the Act’s scope and implementation rules; organizations should assess their own position rather than assume the same legal duties apply everywhere.
Who should own the policy, and when should it change?
Name one accountable policy owner and an approval group with the right business, legal, privacy, security, and operational expertise. Set a review interval, keep the approved-tool register current, and trigger an earlier review after a material incident, a new tool or use, a change in data processing, or a relevant legal or contractual change.
When a tool changes its capabilities, data handling, or retention settings, assess whether its existing approval still fits. Retire obsolete approvals and safely decommission systems that should no longer be used. NIST governance guidance emphasizes documented roles and responsibilities, communication, inventory, monitoring, and periodic review.
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ICO guidance is under review, and EU AI Act duties depend on scope and implementation. Requirements also vary by sector, purpose, data, and jurisdiction. Treat frameworks and agency examples as inputs to a policy tailored to your actual obligations, and obtain local legal review before treating a general template as a complete compliance program.
What employee checklist should the policy put within reach?
Put a brief checklist beside the approved-tool register or in the workflow where employees make AI-use decisions:
- Is this tool approved for this task?
- Does the prompt contain personal, confidential, regulated, proprietary, or security-sensitive information?
- Could the output affect someone’s employment, rights, safety, money, or access to services?
- Have I checked facts, sources, bias, accessibility, privacy, intellectual property, and security?
- Do I need to disclose AI assistance or preserve a record?
- If I am uncertain, have I stopped and asked the policy owner or privacy or security contact?
The checklist is a prompt to use the policy, not a substitute for it. Link it to the approved-tool register, escalation contacts, and incident-reporting route so an employee can move from a question to the right next action.
Frequently Asked Questions
Should an AI use policy cover AI features built into software we already use?
Yes. AI may arrive as a feature inside a familiar product rather than as a separate tool, so define scope to include embedded features and third-party systems. Ask product owners to notify the policy owner when vendors add or materially change AI capabilities.
Can employees use a personal AI account for company work?
The policy should explicitly address personal accounts and devices rather than leaving employees to infer the rule. If the account is not approved for the task, employees should not use it for company work or enter company information into it.
Does an AI use policy replace an organization’s other policies?
No. It should explain how existing privacy, security, records, intellectual-property, accessibility, and conduct rules apply when AI is involved. If two rules conflict, tell employees which policy owner or compliance contact can resolve the question.
Should contractors follow the same AI rules as employees?
State directly whether contractors and other third parties are covered, and align their requirements with contracts, data-access terms, and the work they perform. Make sure the responsible manager communicates the applicable rules before granting access to organizational systems or information.
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