A useful employee AI policy names approved tools, sets clear limits on what employees may enter, requires human review, and explains when to seek approval or report a problem. Build it around your organization’s data, work, and legal obligations—not a generic promise that AI use is either safe or forbidden.
There is no universal employee-policy template in NIST’s guidance. The NIST AI Risk Management Framework is voluntary, and its Generative AI Profile offers risk-management actions organizations can adapt to their goals and resources. Use those materials as a planning aid, then write rules employees can actually follow.
Start with a policy model that fits the work
Before drafting clauses, decide how much freedom employees should have and where the organization needs stronger controls. A tiered model is often a practical starting point: ordinary, low-impact tasks can have fewer approval steps, while sensitive data and decisions affecting people receive more oversight. That is a policy-design recommendation, not a rule mandated by NIST.
| Policy approach | What it permits | Main trade-off |
|---|---|---|
| Blanket ban | No generative AI for work, or only a narrow exception list. | Simple to communicate, but difficult to apply if employees still encounter tools through ordinary workflows. |
| Unrestricted use | Employees choose tools and tasks with few central limits. | Low approval burden, but leaves staff to judge data, accuracy, and impact risks without consistent guardrails. |
| Tiered approval | Routine uses are allowed in approved tools; sensitive or consequential uses require review or approval. | Requires the organization to define categories, owners, and a workable approval route. |
Choose by considering the information employees handle, the potential impact on customers or staff, the organization’s sector and jurisdictions, and the time available for review. Make the rules specific enough that an employee can determine what to do before pasting information into a tool.
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Set the policy’s scope and ownership
Define who and what the policy covers
State whether the policy applies to employees, temporary staff, contractors, and other people doing work for the organization. Clarify that it covers generative AI used for work whether accessed through a company account, a personal account, a browser, an app, or a feature embedded in another product. Define generative AI in plain language, with examples relevant to your workplace, such as text, image, audio, video, or code generation.
Name the policy owner and help route
Identify the team responsible for maintaining the policy and the people who can answer questions—for example, IT or security for tools, privacy for personal data, and legal or compliance for regulated or high-impact uses. Give employees a real contact method or internal route, and explain that asking before use is preferable to guessing.
Make tool approval and information handling explicit
List approved tools or explain how to get one approved
Publish a current list of services and the work purposes for which each is approved. If the list changes often, link to the maintained internal register and identify the owner who updates it. Tell employees not to assume that a public consumer service has been reviewed for company use. Explain how to request a new tool, including what information the reviewer needs: intended task, data types, users, integrations, and expected output.
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Classify information before employees use it
Connect AI use to existing security, privacy, records-retention, and contract rules. Give concrete examples for each category, rather than relying on terms such as “sensitive” without explanation.
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- Public information: Identify whether it may be used in an approved tool for routine drafting or summarization.
- Internal or confidential information: State whether use is prohibited, limited to named tools, or requires approval. Include business plans, source code, pricing, and nonpublic customer or supplier material if those categories apply to your organization.
- Personal, regulated, or otherwise restricted information: Set a clear default—such as no entry unless a named reviewer has approved the use and the tool and process are authorized for that information.
Do not imply that a vendor setting, such as opting out of model training, automatically makes a disclosure lawful or compliant. Check the relevant agreement, security controls, applicable law, and the organization’s existing data rules.
Require human review and assign responsibility
Make a named employee accountable for checking AI output before relying on it, sending it externally, publishing it, or using it in a decision. A tool’s answer is not verified just because it sounds confident or includes citations. The reviewer should check factual claims, calculations, links, tone, and whether the output reveals confidential information or reproduces material that should not be used.
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Scale review to the risk. For a low-stakes first draft, a competent user may check and edit the result. For advice, code, customer-facing material, or work with legal, financial, safety, privacy, or reputational consequences, require review by someone with the appropriate subject expertise. State who owns the final decision; do not let “the AI recommended it” substitute for accountability.
Set a separate approval path for decisions affecting people
Require advance review before using generative AI in hiring, performance evaluation, promotion, discipline, termination, or another consequential decision about an individual. Identify who must approve the use and what oversight is required; depending on the use, the organization may need legal, HR, privacy, accessibility, or compliance review. Do not allow an AI output to make or determine such a decision without the organization’s approved process and meaningful human oversight.
Legal requirements depend on the location and use. The EEOC’s background-check guidance is not AI-specific, but it says employment decisions based on background information must comply with federal nondiscrimination law. Use it narrowly; it is not a substitute for checking applicable AI- and employment-specific rules for the organization’s jurisdiction.
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Explain disclosure, copyright, and third-party content rules
Tell employees when to disclose AI assistance
Set disclosure rules by audience and context: for example, customer or public communications, work subject to a contract, and material governed by professional or sector rules. Specify who approves the wording and where a disclosure should appear. Avoid a blanket statement that every AI-assisted internal document must be labeled.
For organizations subject to EU rules, the European Commission’s July 20, 2026 guidance says Article 50 transparency obligations apply from August 2, 2026, to specified AI-system uses. The Commission’s companion code describes particular covered content, including certain deepfakes and specified public-interest text without human review or editorial control. Applicability depends on the system, role, and content; these sources do not establish a disclosure duty for every internal AI-assisted document. Review the European Commission transparency guidance and Code of Practice on AI-generated content against the actual use.
Keep intellectual-property language cautious
Require employees to follow existing copyright, licensing, confidentiality, and attribution rules when entering material into a tool or using its output. Ask them to seek review when rights are uncertain, especially before publishing or distributing output that resembles third-party material. Do not promise that prompts alone make an output copyrightable: the U.S. Copyright Office’s 2025 report says protection may apply when a human author determines sufficient expressive elements, while merely providing prompts is not enough by itself. That report does not settle every jurisdiction’s law or every infringement question.
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Provide a usable incident and escalation process
Tell employees what to do if they enter restricted information, receive harmful or discriminatory output, discover a fabricated citation or other material error, or suspect that an output exposed confidential information. Give them a reporting channel, name the team that receives the report, and state that they should report promptly rather than trying to conceal or quietly repair a serious incident. Align the process with existing security, privacy, and incident-response procedures.
Employees should also know when to stop and ask for review: unclear data classification, an unapproved tool, a high-impact use, uncertain rights, or an output they cannot verify. A short decision path is easier to follow than a general instruction to “use AI responsibly.”
Use this adaptable policy outline
The following is a drafting scaffold, not legal advice or a substitute for your organization’s existing rules. Replace bracketed instructions with local details, remove categories that do not apply, and have the appropriate owners review it before publication.
- Purpose and scope: “This policy applies to [covered workers] using generative AI for [covered work]. It covers [approved channels and embedded features]. The policy owner is [team]; questions go to [contact or route].”
- Approved tools: “Use only services listed in [internal register] for work. To request a tool or use not listed, contact [owner] with the task, data involved, and intended users. Do not use a personal account for work unless expressly approved.”
- Information handling: “Follow the organization’s information-classification, privacy, security, retention, and contractual rules. Do not enter [restricted categories] unless [named approval and tool conditions] are met.”
- Review and accountability: “A responsible employee must verify AI-generated content before relying on it or sharing it. The employee remains accountable for the final work. [Higher-risk outputs] require review by [role or team].”
- Consequential uses: “Do not use AI to make or materially determine decisions about [listed people-related decisions] without advance approval from [roles] and the oversight described in [process].”
- Disclosure and rights: “Disclose AI assistance when required by [law, contract, professional rule, or organizational standard]. Follow existing intellectual-property and attribution requirements; seek review when rights are uncertain.”
- Reporting and training: “Report accidental disclosure, harmful output, or suspected misuse through [channel] promptly. Complete [training] and check [policy location] for current rules.”
Put the policy into practice and keep it current
Publish examples alongside the rules
Use short workplace scenarios to show how the policy applies: drafting a public FAQ from approved public material, summarizing confidential customer correspondence, generating code, or screening job applicants. For each, state whether it is allowed, what information may be used, what review is required, and where to ask for approval. Examples should reflect your actual tools and information categories.
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Show employees how to find approved tools, classify information, verify output, disclose use when required, and report incidents. Assign an owner to review the policy when tools, contracts, organizational practices, or applicable law change. NIST’s framework supports ongoing organizational risk management; the organization chooses the review cadence and process. NIST says the AI RMF is voluntary and notes that revision is in progress. See the NIST AI Risk Management Framework and its Generative AI Profile, released July 26, 2024, for adaptable risk-management material.
For UK personal-data uses, check the Information Commissioner’s Office’s current AI and data protection guidance before stating a definitive compliance position. The ICO says the guidance is under review following the Data (Use and Access) Act and distinguishes its legal interpretation from good-practice recommendations. See the ICO guidance overview and confirm the law applicable to the specific use.
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