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An AI policy employees can follow is a short set of operational decisions: which tools are approved, what information may go into each one, which tasks are allowed, when a person must review the result, when use must be disclosed, and how to report a mistake. Start with those decisions in plain language, assign an owner for approvals and incidents, and train people with examples from their actual work. Use the NIST AI Risk Management Framework (AI RMF) to organize the program, but treat it as voluntary guidance rather than a ready-made handbook or legal requirement.
What should an employee AI policy cover?
Define the policy’s audience, systems, and purpose before listing rules. State that it applies to employees, contractors, temporary staff, and anyone else performing work for the organization. Explain whether “AI” includes public chatbots, AI features built into office applications, coding assistants, transcription and meeting tools, image generators, automated decision systems, and systems the organization builds itself.
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Use ordinary definitions that match your environment. A narrow definition that covers only a chatbot can leave employees unsure about an AI writing feature inside an approved document suite; a sweeping technical definition can make the policy impossible to apply. NIST’s Govern guidance recommends defining key terms, purposes, and intended uses while tailoring them to organizational context. See the NIST AI RMF Govern guidance.
Give the document a visible owner, an effective date, and a review date. Identify the team that can approve tools and the contacts for privacy, security, legal, human resources, accessibility, procurement, and communications questions. Employees should be able to answer “Who decides?” without searching through several procedures.
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How do I publish an approved-tools list employees will use?
Maintain one dated, searchable inventory rather than asking employees to infer approval from a software catalog or a purchase record. For every tool, list the approved work purposes, the data categories it may process, prohibited uses, the account or configuration required, and the team responsible for reviewing changes.
Adopt a simple default: if a tool is not on the list, employees must not use it with organizational information until it has been reviewed. Permit experimentation with public, non-sensitive material only if your policy explicitly allows it. Give employees a request route with a form or service-desk category, a decision owner, and an expected response time; otherwise they will make informal exceptions.
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Examples from government guidance illustrate the pattern without automatically binding private employers. Miami-Dade directs employees to use county-approved generative AI tools, while the IRS requires approved and accountable agency use. Read the Miami-Dade employee policies and the IRS AI governance policy (revised February 10, 2026) as design examples, then set rules appropriate to your organization.
Record approval conditions, not just a product name. A tool may be acceptable for public marketing copy but not for customer records, source code, health information, or employment decisions. Recheck terms, retention, model-training settings, administrator controls, and subprocessors when a vendor changes its service.
Can employees put company data into ChatGPT or another AI tool?
Only when the specific tool and use have been approved for that information. A policy should make the boundary memorable by grouping information into categories employees recognize and by giving a clear escalation path for uncertainty.
| Information category | Typical examples | Default employee instruction |
|---|---|---|
| Public information | Published webpages, press releases, public filings | May be used in an approved tool for an approved task; verify that the material is genuinely public. |
| Internal, non-sensitive information | Routine internal drafts or procedures without personal, confidential, or regulated details | Use only tools approved for internal content and follow the listed retention and account settings. |
| Confidential business information | Unreleased plans, pricing, contracts, credentials, security details, source code, trade secrets | Do not enter unless the tool and exact use are explicitly approved for confidential data. |
| Personal information | Names linked to records, contact details, identifiers, employee or customer files | Keep out of public tools; obtain privacy approval for any approved processing. |
| Regulated or highly sensitive information | Protected health information, financial-account data, government identifiers, legally restricted records | Do not enter unless the organization has approved the tool, safeguards, legal basis, and workflow for that category. |
| Third-party or copyrighted material | Customer submissions, licensed databases, paid reports, code or images supplied under contract | Confirm that the organization has the right to submit and reuse the material; ask legal or procurement when unsure. |
Do not rely on an apparently harmless prompt. The IRS warns that information that seems innocuous can still create risk in public or non-Treasury tools. CMS tells its personnel not to enter personally identifiable information, protected health information, or sensitive CMS data into publicly accessible AI platforms. Those agency restrictions are examples, not automatic rules for every employer. Consult the CMS responsible-use guidance and your own privacy and security requirements.
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Explain why settings matter. A consumer account, an enterprise account, an API integration, and a locally hosted model may have different retention, training, access, and deletion behavior. Require employees to use the organization’s managed account or configuration where approval depends on those controls.
What should employees be allowed to use AI for?
Classify work by consequence, not by whether the output is text, code, an image, or a prediction. Low-consequence assistance can usually proceed in an approved tool with ordinary review. Work that affects rights, eligibility, safety, money, employment, or a public commitment needs explicit controls and often a separate approval.
Lower-risk uses with ordinary review
- Brainstorming ideas or outlines without confidential inputs.
- Summarizing public material that an employee has read or can verify.
- Drafting routine internal text, meeting agendas, or plain-language explanations from approved source material.
- Generating test data that contains no real personal or confidential information.
- Explaining code, suggesting non-production snippets, or helping document a system when proprietary code is handled in an approved environment.
Uses that require a defined review or approval
- Customer-facing, investor-facing, regulatory, safety, medical, financial, or contractual communications.
- Recommendations that determine eligibility, access, pricing, benefits, insurance, credit, housing, education, or other consequential outcomes.
- Hiring, promotion, performance, discipline, scheduling, termination, or workplace-surveillance decisions.
- Security operations, fraud detection, incident response, or actions that could change production systems.
- Any use involving protected, regulated, confidential, or third-party material beyond the tool’s approved data boundary.
Write the condition beside each use. “Draft customer email” might be allowed only when an employee checks every factual claim and removes personal data; “screen applicants” might be prohibited unless HR, legal, accessibility, and security owners approve a documented process. NIST’s Generative AI Profile (NIST AI 600-1, July 2024) recommends transparent acceptable-use policies addressing illegal uses and applications, with controls matched to organizational priorities.
How should a policy require human review of AI output?
Make a named employee accountable for every output used in work. AI output is a draft or aid, not verified authority. The reviewer must check the result against reliable sources, test calculations or code, assess bias and appropriateness, confirm that citations and permissions are real, and remove information that should not be shared.
- Check factual accuracy. Trace important claims to authoritative documents or data; do not accept fluent wording as evidence.
- Check fit and fairness. Look for stereotypes, exclusions, unsafe instructions, inaccessible language, and assumptions that do not apply to the audience.
- Check confidentiality and rights. Ensure prompts and outputs contain no unauthorized personal or confidential information and that reuse complies with licenses, contracts, and copyright rules.
- Check the action. Confirm that a person with the required role and expertise makes the final decision, especially where someone’s rights, safety, employment, or money is affected.
- Keep the needed record. For material decisions or external communications, retain the prompt or input description, tool and version, reviewer, source checks, edits, and approval where your records policy requires it.
CMS says personnel should not rely solely on generated output for policies or decisions and calls for human oversight before business decisions or external sharing. The IRS assigns users responsibility for their inputs and for the use, publication, or distribution of outputs, including review before further use. Your policy can adopt the same accountability principle without copying federal procedures.
What extra controls are needed for hiring and employment decisions?
Treat employment uses as a high-scrutiny category. A model that ranks applicants, analyzes video or voice, recommends discipline, or predicts performance can affect legal rights and may screen out qualified people with disabilities. Separate approval should identify the business purpose, data source, validation evidence, reviewer, appeal route, and accommodation process.
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Before and during use, examine whether the technology disadvantages people because of a disability and whether an applicant or employee can request a reasonable accommodation. ADA.gov’s guidance on algorithms, artificial intelligence, and disability discrimination in hiring describes these risks. The EEOC’s guidance on visual disabilities in the workplace addresses accommodation considerations for tools affecting applicants or employees.
Provide a non-automated way to seek review or accommodation, tell HR how to pause a tool when a problem is suspected, and prohibit managers from treating a model score as the sole basis for an adverse action. Applicability depends on the employer, tool, use, and jurisdiction; obtain legal review rather than treating these federal sources as a complete employment-law checklist.
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Set disclosure rules by audience and consequence instead of imposing one universal label. Disclosure may be appropriate for customer deliverables, regulated submissions, research, educational content, or a contract that requires it. Internal brainstorming may not need a notice if the employee remains responsible for the finished work. Specify who decides and provide approved wording or a record field when disclosure is required.
Require employees to respect copyright, licenses, confidentiality clauses, and rights of publicity. Do not upload a customer’s submission, a paid database extract, licensed code, or a colleague’s private work merely because a tool accepts the file. Before publishing generated text, images, audio, or code, confirm that the organization has the necessary rights and that a human has checked for copied or restricted material. Miami-Dade’s guidance directs staff to respect copyrights and seek legal advice when uncertain; see its employee policy page.
How should employees report an AI mistake or request an exception?
Put one reporting channel in the policy and repeat it in the approved-tools list and training. Tell employees to report accidental data submission, unexpected disclosure, unsafe or discriminatory output, suspected security compromise, fabricated or materially wrong content, and any use outside an approval condition.
Give immediate containment instructions that fit your organization: stop further sharing, preserve relevant prompts or logs without circulating sensitive data, notify the service desk or security contact, and tell a manager or privacy officer when personal or regulated information may be involved. Do not promise that one universal response sequence applies to every employer. CMS, for example, directs personnel to report suspected or verified privacy breaches through its service desk and applicable policies; a private organization should publish its own escalation sequence.
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Define an exception process with a decision owner, evidence required, expiration date, and review trigger. An exception should be narrow—specific tool, data, purpose, users, and duration—not a blanket permission. Record the decision so the same question does not have to be re-litigated by every team.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I train employees so they actually follow the policy?
Use short, role-based training built around decisions employees make, not a lecture about model architecture. A general module can teach the approved-tools rule, data categories, review duties, disclosure, and incident reporting. Add scenarios for developers, customer support, recruiters, analysts, managers, and executives.
- Show a safe prompt using public information and an unsafe prompt containing personal or confidential data.
- Have learners classify a proposed task as allowed, approval-required, or prohibited.
- Practice finding a hallucinated citation, biased recommendation, or accessibility problem.
- Demonstrate the request form, incident channel, and accommodation route.
- Require acknowledgement and refresh training when a material rule or tool changes.
Measure completion and practical understanding, not just attendance. Managers should know which uses their teams have approved and how to stop an unsafe workflow. NIST’s Govern guidance emphasizes roles, training, monitoring, incident response, and periodic review.
Who owns the policy and when should it be updated?
Name a policy owner with authority to coordinate security, privacy, legal, HR, accessibility, procurement, records, and communications. The owner should maintain the tool inventory, publish change notices, track exceptions and incidents, and schedule reviews.
Review at a defined cadence—at least annually for a stable program and sooner when a major model, integration, data practice, law, incident, or business use changes. Revalidate each tool’s terms, retention, access controls, model-training choices, and approved data categories. Retire entries for tools that are no longer supported or no longer meet the organization’s requirements.
NIST’s AI RMF is a voluntary organizing resource, not a legal mandate. The NIST AI Risk Management Framework status page, accessed October 25, 2026, says AI RMF 1.0 is being revised and notes an April 7, 2026 concept note for a critical-infrastructure profile. The NIST AI RMF Playbook says it will be updated after the framework is revised. Check those pages when your policy is reviewed. The Playbook itself cautions: “The Playbook is neither a checklist nor set of steps to be followed in its entirety,” and “Playbook suggestions are voluntary.”
How should an organization choose between a strict and flexible policy?
There is no single model established by the guidance. Choose controls by weighing six questions for each proposed use:
- What data is involved? More sensitive data requires stronger technical and contractual controls.
- What is the consequence? A spelling suggestion is different from a recommendation affecting rights, safety, employment, or money.
- Who sees the result? Public-facing and regulated outputs need more review than private brainstorming.
- Who is using it? A trained specialist may be able to validate a coding or analytical output that another role cannot safely assess.
- Can the result be verified and recorded? If you cannot test, explain, or retain the basis for an important output, add controls or do not use the system.
- What is the approval burden? Match review effort to risk so employees do not bypass a process that treats every harmless draft like a high-impact decision.
A practical model is tiered: allow low-risk work in listed tools, require documented review for consequential work, and prohibit uses that cannot meet privacy, security, fairness, accessibility, legal, or rights requirements. Reassess a tier when the tool, data, audience, or purpose changes.
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What can a concise policy say?
The following clauses can be adapted to your organization’s names and contacts:
- Approved tools: “Use only tools on the current approved list for organizational work. If a tool is absent, submit a review request before entering organizational information.”
- Data: “Do not enter confidential, personal, regulated, security-sensitive, or third-party restricted information unless the listed tool and use are approved for that category.”
- Allowed work: “Use approved tools for the purposes listed for that tool. Obtain the required review before using AI for employment, eligibility, safety, financial, legal, customer-impacting, or public-facing decisions.”
- Human responsibility: “A responsible employee must verify accuracy, suitability, fairness, accessibility, confidentiality, and rights before using or sharing an output.”
- Disclosure: “Disclose AI assistance when this policy, a contract, law, regulator, customer, or recipient requires it.”
- Reporting: “Report accidental data submission, harmful or discriminatory output, suspected security or privacy incidents, and policy violations through the designated channel immediately.”
- Exceptions: “Exceptions require written approval that identifies the tool, data, purpose, users, safeguards, owner, and expiration date.”
Publish the clauses with the approved-tools inventory, request form, incident contact, accommodation route, and review schedule. That combination turns broad principles into choices an employee can make at the moment of work.
Frequently Asked Questions
Should a small business ban public AI chatbots entirely?
A blanket ban is one option, but it is not the only workable control. A small business can permit public tools for public information while prohibiting personal, confidential, regulated, and customer data, then require a managed account or review before expanding use.
What evidence should we keep when AI helps make a business decision?
Keep enough information to reconstruct the decision: the tool and version, purpose, data sources, material prompt or input description, human reviewer, edits, and final approval. Apply your existing records-retention and privacy rules, and avoid retaining raw sensitive prompts when a safer summary will document the same rationale.
How can employees tell whether an AI feature inside approved software is covered?
State in the scope that embedded features count unless the policy expressly excludes them. The approved-tools inventory should identify the specific feature, account configuration, permitted data, and owner; an employee who cannot find that detail should pause and ask the listed contact.
Does following the NIST AI RMF make an organization legally compliant?
No. NIST describes the AI RMF and Playbook as voluntary resources for organizing risk management. Privacy, employment, accessibility, copyright, security, sector, and jurisdictional duties still depend on the organization’s facts and require appropriate legal and compliance review.
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