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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 concerns. Build it around your organization’s tools, data, roles, risks, and jurisdictions—not as a generic ban or a one-size-fits-all template.
Start with an inventory and an accountable owner
Before setting broad rules, find out where employees already encounter AI. Include tools bought by the organization, third-party services, AI features embedded in products employees already use, and experimentation with public tools. A policy that covers only a standalone chatbot can miss AI that is part of a hiring platform, office suite, customer-service system, or other workflow.
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For each tool or meaningful use case, record its business purpose, the people affected, the kinds of information involved, who owns the business process, and who reviews technical, privacy, and security concerns. Name one accountable policy owner and a route employees and managers can use to ask for approval or report a problem. Keep the inventory current as tools and uses change.
The National Institute of Standards and Technology (NIST) presents its AI Risk Management Framework (AI RMF) as a voluntary way to incorporate trustworthiness considerations into AI design, development, use, and evaluation. It is a flexible governance aid, not a universal employee-policy template or a legal safe harbor. NIST says the AI RMF 1.0 is under revision; its Generative AI Profile, NIST AI 600-1, was released on July 26, 2024.
Match approval and controls to the use
Not every use presents the same risk. A low-impact drafting aid using public information may need fewer controls than a system that influences hiring, employee evaluation, access to services, financial decisions, safety, or legal rights. Classify uses by data sensitivity, who may be affected, the significance of the decision, and whether an error can be reversed. Then set review and approval requirements accordingly.
| Policy choice | What the policy should decide |
|---|---|
| Risk-based rules or blanket restrictions | Whether controls vary by the tool, data, and impact of the use, or whether some uses are barred across the organization. |
| Data permitted in tools | Which data categories each approved tool and configuration may handle, and which categories must stay out of unapproved systems. |
| Human review | Who verifies outputs and who must approve consequential uses before an output informs or drives a decision. |
| Employee autonomy or pre-approval | Which routine uses employees may begin under standing rules and which require approval before use. |
| Documentation | What the organization records about approved tools and use cases, reviews, and material incidents. |
| Coverage | Whether rules apply to third-party services, embedded features, and employee experimentation as well as organization-provided AI. |
| Review frequency | Who checks the policy and inventory, and what changes trigger an earlier review. |
These are design decisions, not a universal scoring system. NIST offers a voluntary, flexible framework; Federal Trade Commission (FTC) and Equal Employment Opportunity Commission (EEOC) materials describe agency-specific governance examples. None by itself supplies a complete policy for every private employer.
Write rules employees can apply
Define scope and approved use
State who is covered, what the organization means by AI, and whether the policy covers generative AI, automated decision tools, AI inside third-party products, and experimentation. Maintain an approved-tools list that names permitted purposes and any restrictions, not just product names. Require review before procurement, integration, or use with organizational data. Identify the business owner and the technical, privacy, and security reviewers for each approved use.
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Set data boundaries
Map the organization’s data categories to tools and settings employees are allowed to use. Explicitly prohibit placing confidential, personal, regulated, or otherwise restricted information into a tool that has not been approved for that type of data. Address access controls, retention, and whether a vendor may use submitted data, including for service improvement or model training. Make clear that an employee’s ability to access information internally does not by itself make that information permissible to enter into an AI service.
The FTC’s agency plan emphasizes preventing unauthorized exposure of nonpublic data. For employees, the operational rule should be easy to follow: when a tool or data category is not approved, do not enter the information; ask the designated owner or escalation contact instead.
Require output checks and human approval
AI output can be inaccurate, incomplete, misleading, or fabricated. Require employees to check factual claims against dependable sources and verify calculations, citations, and material details before using or sharing an output. The person using the output remains responsible for work product and professional obligations; a plausible answer is not evidence that it is correct.
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Set a higher bar where an output could affect people, customers, finances, safety, or legal rights. Specify who reviews the output, what they must verify, and who has authority to approve the final decision. Do not allow unsupervised reliance on AI for consequential employment, legal, safety, or customer decisions unless that specific use has been separately assessed and authorized. Define prohibited decisions and escalation routes in plain language rather than relying on a general instruction to “use judgment.”
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Require an impact review when a use may affect hiring, evaluation, access, or other rights. Consider who could be disadvantaged, whether the inputs and outputs are appropriate, and whether a human reviewer can meaningfully challenge or correct an outcome. The EEOC’s 2025 plan describes civil-rights impact considerations, governance, an inventory, and minimum-risk practices for its own high-impact cases. That is an agency example, not a complete account of private-employer duties.
Set expectations for protecting personal information, limiting access, and disclosing AI involvement when law, a contract, or organizational policy requires it. Tell employees when they must disclose AI assistance to a manager, customer, colleague, or other recipient, and when a human review must be visible in the workflow. Apply existing conduct and anti-discrimination rules to AI-assisted work as well as work produced without AI.
Protect intellectual property and professional obligations
Require employees to review generated material before using it, including checking for unsupported claims, confidential information, and material that may raise attribution or rights concerns. Define when provenance or a record of AI assistance is needed. Make clear that using AI does not remove sector-specific, contractual, or professional responsibilities. The FTC’s agency plan, for example, calls attention to plagiarism and the obligations of agency attorneys; organizations should identify the corresponding duties that apply to their own work.
Make incidents and exceptions actionable
Name the policy owner and give employees a clear escalation channel for questions, exceptions, suspected data exposure, harmful or discriminatory output, security events, and policy violations. Explain what to do immediately—such as stopping the affected use and reporting it through the designated channel—and what information to preserve. Record approved use cases and material incidents so owners can assess patterns and update controls. Do not make employees guess whether an issue is serious enough to report.
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- Inventory current use. List AI-enabled products and employee use cases, including embedded features and experimentation.
- Classify each use. Assess data sensitivity, affected people, decision impact, and reversibility.
- Approve tools and configure controls. Select permitted tools and set data permissions, access, retention, and other technical safeguards.
- Write plain-language rules. State permitted uses, restrictions, approval exceptions, human-review responsibilities, and reporting paths.
- Train employees and managers. Use role-specific examples covering approved tools, data handling, output verification, bias and privacy concerns, and incident reporting.
- Monitor and revise. Track material incidents and changes in tools, risks, and obligations; update the inventory, controls, training, and policy when needed.
Training should distinguish what employees may do from what requires approval, and show how to check an output and report a concern. The FTC’s agency plan identifies AI fundamentals and ethical considerations as training topics. Tailor examples to actual roles and workflows rather than assuming a single briefing will answer every employee’s questions.
Review the policy against changing obligations
Set a named owner and a regular review process, with an additional review when a tool, use case, law, or risk changes materially. Check requirements for each jurisdiction and use case. Employment, privacy and data protection, consumer protection, intellectual property, sector rules, collective agreements, and local law can all affect what a policy must require.
The European Commission published guidance on Article 50 transparency on July 20, 2026, and says the obligations apply from August 2, 2026. That date does not determine whether a particular employer or workplace use is covered; applicability requires legal analysis. Likewise, Federal agency plans from the FTC and EEOC are useful examples of governance concerns, not a substitute for reviewing the rules that apply to your organization.
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