A responsible employee AI policy should tell people which tools and uses are approved, what information they may enter, when a person must check or approve an output, and how to raise a concern. Build it around your organization’s actual tools, data, work and jurisdictions—not as a generic ban or a copied template.
Start with governance, not a list of chatbot rules
Before drafting, find out where AI is already being used and who is accountable for each use. Include tools employees select themselves as well as AI features built into software the organization already licenses. A policy cannot govern uses its owners do not know about.
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Assign a policy owner—often a function with authority to coordinate legal, privacy, security, IT and business teams—and define who can approve tools and use cases. NIST’s AI Risk Management Framework (AI RMF) offers a voluntary way to organize risk work across the AI lifecycle; it is not a legal safe harbor or a universal employee-policy template. NIST says the AI RMF 1.0 is being revised. Its Generative AI Profile, NIST AI 600-1, was released July 26, 2024.
Federal agency plans can provide examples of governance, not a complete checklist of private-employer obligations. The EEOC’s 2025 plan describes inventory, governance and ongoing updates, while the FTC’s agency plan addresses risks including unauthorized exposure of nonpublic data and inaccurate or hallucinated outputs.
Choose rules that match the risk
Decide how restrictive the policy should be by considering the information involved, the people affected, the consequences of error and the organization’s ability to review or reverse an outcome. A blanket restriction can be easier to communicate, but may be less practical than clear approvals and controls for different uses. The table below is a design aid, not a prescribed legal standard.
| Policy choice | Questions to settle |
|---|---|
| Risk-based rules or blanket restrictions | Will rules vary according to the use and likely impact, or will AI use be broadly prohibited except for explicit exceptions? |
| Data permitted in tools | Which data categories may be entered into which approved tools and settings? What must stay out? |
| Human review | Which outputs require verification, and which consequential uses require a named person’s approval? |
| Employee autonomy or pre-approval | Can employees use approved tools for defined low-risk tasks, or must each use case be approved in advance? |
| Documentation | What approvals, material uses and incidents must be recorded so owners can review them? |
| Tool coverage | Does the policy cover third-party services, embedded features, integrations and employee experimentation? |
| Review cadence | Who checks the policy and approved-use records, and what changes trigger an earlier review? |
NIST is designed to be flexible and voluntary. The FTC and EEOC materials describe agency-specific approaches; they do not settle which design is right for a particular employer.
Rank #2
What the written policy should cover
Use plain language and make the policy actionable: employees should be able to tell what to do, what not to do, and where to go when a rule does not fit.
| Policy section | What to specify |
|---|---|
| Purpose and scope | Who is covered; what the organization means by AI; and whether third-party tools, AI embedded in existing products, integrations and experimentation are included. |
| Approved tools and uses | Where employees can find the current approved list; which uses are authorized; who owns each use; and which reviewers must assess procurement, integration or use with organizational data. |
| Data handling | Data categories, permitted tools and settings, access and retention expectations, and whether a vendor may use submitted information. Prohibit entering confidential, personal, regulated or otherwise restricted information into a tool not approved for that data. |
| Output checks and decisions | How employees verify outputs against dependable sources, who approves use in consequential contexts, and which decisions cannot rely on unsupervised AI unless separately assessed and authorized. |
| Fairness, privacy, security and transparency | How likely effects on people are assessed, how personal information and access are protected, and when employees must disclose AI involvement or arrange human review. |
| Intellectual property and professional duties | How generated material is reviewed, when its origin must be recorded, and how sector-specific and professional obligations apply. The FTC agency plan, for example, flags plagiarism and duties of agency attorneys. |
| Accountability and incidents | The policy owner, approval and exception route, reporting channel, documentation expectations, and response path for harmful outputs, disclosures, security events or violations. |
| Training and updates | Who needs role-specific training and how the policy is revisited as tools, risks and applicable rules change. |
Set boundaries for data and outputs
Match data permission to the tool
Do not treat approval of a tool as permission to put every kind of information into it. Maintain a usable mapping between data categories and permitted tools or settings. State what restricted information employees must not submit to unapproved systems, and explain where to check if they cannot tell whether a tool or data type is allowed. Include relevant retention, access and vendor-use expectations in the approval decision.
Rank #3
Require verification before relying on an output
AI-generated material can be false, incomplete or misleading. Require employees to check important factual claims against dependable sources before using them, and to apply the same professional standards they would apply to work produced without AI. For decisions affecting employees, applicants, customers, finances, safety or legal rights, identify the human approver and the required level of review. Do not permit unsupervised reliance in those consequential contexts unless the use has been separately assessed and authorized.
Assess effects on people
When AI may affect hiring, evaluation, access or other rights, require an appropriate review of likely impacts, including fairness and privacy concerns. The EEOC’s 2025 plan discusses civil-rights impact and minimum risk practices for its high-impact cases. That is an agency example, not a complete statement of what private employers must do.
Put the policy into operation
- Inventory use. Record AI-enabled products and meaningful employee use cases, including features embedded in existing products. Give each use an accountable business owner.
- Classify risk. For each use, consider data sensitivity, affected people, decision impact and whether an outcome can be reversed or corrected.
- Approve tools and controls. Decide which tools and use cases are allowed, set data permissions and technical controls, and identify the reviewers needed before procurement, integration or use with organizational information.
- Write the rules and routes. Explain permitted use, prohibited data, human review, exception approval and incident reporting in language employees can follow.
- Train by role. Give employees and managers examples relevant to their work, including allowed tools, data handling, output checking, bias and privacy concerns, and how to report an incident. The FTC agency plan also calls out AI fundamentals and ethical considerations for staff.
- Monitor and update. Review records and incidents, and revise the approved uses, controls or policy when tools, risks, evidence or obligations change.
Check jurisdiction-specific obligations before rollout
An internal policy cannot replace analysis of the laws and obligations that apply to a particular employer, location and use case. Employment, privacy and data-protection, consumer-protection, intellectual-property and sector rules may matter, as may collective agreements, professional duties and local law. Have qualified counsel assess the uses in scope rather than treating a general framework or another organization’s policy as legal clearance.
For employers with relevant EU activity, the European Commission published Article 50 transparency guidance on July 20, 2026, and says the obligations apply from August 2, 2026. Whether a particular workplace use falls within those obligations requires legal analysis; the date alone does not classify a tool or use.
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Make one owner responsible for keeping the approved-tool and use-case records current, receiving escalations and coordinating policy review. Revisit the rules after a material change in a tool or its settings, a significant incident, a new use with greater impact, or a relevant change in applicable obligations. NIST’s AI RMF can help organize that ongoing risk-management work, but adopting it does not itself establish compliance.
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