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Use AI at work only when the tool is approved for the task and the information involved, and set stronger human review as the consequences rise. A workable boundary tells employees which tools and uses are allowed, what information must stay out, who checks the output, who makes the final decision, and how to report a problem.
What makes a workplace AI use acceptable?
Assess a proposed use across four dimensions: the sensitivity of the information entered, the consequences of an error or disclosure, the quality of human review and ability to correct an outcome, and how clearly the tool’s data practices and third-party dependencies are understood. These are practical decision factors drawn from NIST risk guidance and U.S. Department of Labor workplace best practices—not a universal legal test.
- Input sensitivity: Does the prompt or uploaded file contain personal, confidential, proprietary, client, employee, or otherwise restricted information?
- Impact: Could a wrong, biased, or exposed output harm a person, the organization, or a third party?
- Review and reversibility: Can a qualified person check the output before anyone relies on it, and can an outcome be corrected?
- Tool visibility: Do you understand how the service handles data and what third parties or integrations are involved?
When sensitivity, impact, or uncertainty is high, restrict the use, require stronger review, or do not use the tool for that task. NIST’s Generative AI Profile identifies privacy, information-security, intellectual-property, and third-party risks, and recommends acceptable-use guidance for generative AI.
How should you set a boundary?
1. Name the task and its outcome
Be specific about what the AI will do and what people may do with its output. Drafting, summarizing, brainstorming, coding assistance, and information retrieval are different from making or determining an outcome. NIST’s profile describes uses including code generation and review, text generation and editing, summarization, search, and chat. A rule that says “AI is allowed” is too broad to guide these different activities.
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2. Classify the information before entering it
Check whether the prompt, file, or conversation contains personal, confidential, proprietary, customer, employee, or third-party material. Then check the specific tool’s data practices and contractual terms. Do not assume that an external service protects company information simply because it is widely used, or that all AI tools handle submitted data the same way. NIST calls for clear guidelines and procurement due diligence for third-party generative AI integrations.
3. Match review to the consequences
Ask who could be harmed if the output is inaccurate, biased, exposed, or used without review. The more significant the consequence, the more robust the review should be. The Department of Labor’s October 16, 2024 best-practices release calls for meaningful human oversight of significant employment decisions. AI output should not silently become the final decision in such cases.
4. Define what the reviewer must do
“Human in the loop” is not enough unless the policy describes the role. Specify what the reviewer checks, what expertise is needed, and who remains accountable for the decision. Depending on the risk and context, NIST says generative AI may call for different levels of human oversight, review, tracking, and management oversight.
5. Assign ownership and keep the rule operational
Name the person or team responsible for approving tools and updating the policy. Explain training, monitoring, appropriate disclosure, and how workers should escalate suspected errors, harmful outputs, or data exposure. NIST’s profile identifies data protection, retention, education, impact assessment, monitoring, and incident response among relevant organizational controls. The Department of Labor also highlights worker transparency and input, worker training, and worker-data security.
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These examples are starting points, not blanket permissions. A use that seems routine can become higher risk if it involves sensitive information, affects a person’s opportunities, or relies on an unvetted tool.
| Proposed use | Questions that shape the boundary | Practical control |
|---|---|---|
| Brainstorming or drafting general material | Does the prompt include restricted information? Is the output being presented as fact or sent externally? | Use an approved tool, keep restricted information out unless explicitly permitted, and review claims and wording before use. |
| Summarizing or searching company material | Is the material confidential, personal, or subject to third-party restrictions? Is the tool approved for that data? | Use only a tool whose data handling and contractual terms have been checked for the information involved; verify the summary against the source. |
| Coding assistance | Could the prompt expose proprietary code, credentials, or security-sensitive details? Will generated code be tested? | Follow the organization’s rules for code and secrets; require appropriate review and testing before generated code is used. |
| Employment decisions or other consequential outcomes | Could the output affect a person’s job, opportunity, or treatment? Can a qualified reviewer challenge and correct it? | Set meaningful human oversight and make clear that the responsible person—not the AI—owns the decision. |
What should a clear workplace policy say?
Employees need rules they can apply before they paste information into a prompt or act on an answer. A concise policy should state:
- Which AI tools are approved, and who approves new tools or integrations.
- Which tasks are allowed, restricted, or prohibited.
- What information may not be entered, and when an approved tool may be used with sensitive data.
- What the reviewer must verify and who has final decision authority.
- When AI use should be disclosed to coworkers, customers, or other affected people.
- How to report an incident, suspected harm, or questionable output.
- Who owns training, monitoring, and policy updates.
NIST states that acceptable-use policies can help reduce risks from misuse, abuse, inappropriate repurposing, and misalignment between systems and users. Its profile covers proprietary and open-source generative AI technologies as well as third-party personnel.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What NIST and the Department of Labor guidance do—and do not—establish
NIST’s AI Risk Management Framework (AI RMF) is voluntary. NIST describes its AI RMF Playbook as suggested actions, not a checklist. The framework page identifies the Generative AI Profile as released on July 26, 2024, and says AI RMF 1.0 is being revised. These resources can help organizations structure risk management, but they do not by themselves determine an employer’s legal obligations.
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The Department of Labor release is dated October 16, 2024 and carries a notice that some information may be out of date or may not reflect current policies. Treat it as dated best-practices guidance; requirements depend on the jurisdiction and the particular workplace use. For framework status and scope, see the NIST AI RMF page, NIST AI RMF FAQs, and NIST AI RMF Playbook.
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