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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsGovern employee use of generative AI with clear acceptable-use rules, a named cross-functional owner, an inventory of approved tools and uses, and controls proportionate to the data and consequences involved. Employees need to know what they may enter, when they must check an output, which uses need approval, and how to report a problem. Review those rules as tools, integrations, and work practices change. NIST’s AI Risk Management Framework (AI RMF) and Generative AI Profile offer voluntary guidance for building that operating model; they do not prescribe one universal employee-use policy or risk-tier scheme.
What should an employee AI governance policy cover?
A policy should be usable at the moment an employee is deciding whether to use a tool or rely on its output. State the rules plainly, identify where to get approval, and give employees a way to report mistakes and suspected exposures. NIST’s Generative AI Profile recommends acceptable-use policies and guidance for different human–AI configurations; the details should reflect the organization’s tools, work, and obligations.
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- Approved tools: Name the services employees may use for work, any approved integrations, and the process for requesting a new tool or use.
- Data rules: Specify what public, internal, personal, confidential, customer, employee, or regulated information may be entered into each approved service. Do not assume that all services have the same retention or data-use terms.
- Permitted, restricted, and prohibited uses: Give concrete examples. Identify work that requires prior review and work the organization does not permit, rather than relying on a vague instruction to “use AI responsibly.”
- Verification and human review: Tell employees what they must check before relying on or sharing an output, and who has authority to make the final decision in consequential work.
- Disclosure: Explain when employees must identify AI-assisted work under organizational policy, law, customer terms, or professional practice.
- Incident reporting: Provide a clear route for reporting suspected data exposure, harmful or misleading output, errors, or policy violations.
- Review: State who maintains the rules and how approved tools and uses are reassessed.
Use examples drawn from actual job tasks. For instance, a rule should distinguish drafting a routine internal outline from generating material that will be sent externally or used to support a consequential decision. Examples make the boundaries easier to apply without implying that every instance of a broad task is equally risky.
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Assign an executive risk owner and operational responsibilities across the teams that manage the relevant risks. Depending on the organization, that may include IT and security, privacy, legal, HR, procurement, and business teams. NIST’s AI RMF Govern function emphasizes clear roles, leadership accountability, communication, trained personnel, and ongoing oversight across the AI lifecycle.
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| Responsibility | What the owner does |
|---|---|
| Executive risk owner | Sets accountability and ensures that significant AI risks have an organizational decision-maker. |
| Tool and use-case approvers | Review proposed services and material uses, record approved boundaries, and set any conditions of use. |
| Risk reviewers | Assess relevant data, affected people, possible consequences, provider terms, integrations, and oversight before approval. |
| Incident responders | Receive reports and coordinate response to suspected exposure, harmful output, or other policy violations. |
| Policy maintainer | Coordinates training, monitoring, and periodic review, including updates after material changes or incidents. |
Keep an inventory of approved tools and material use cases. For each entry, record its purpose, provider, users, data categories, integrations, approval owner, risk assessment, human oversight, and review date. That record helps the organization locate systems, reassess exposure, and retire a tool safely when it is no longer suitable. The NIST AI RMF calls for system inventories, documented impacts, monitoring, and defined roles and review responsibilities.
How can an organization scale controls to risk?
Assess the work, not just the brand or general capabilities of the tool. NIST supports risk-based assessment and controls proportionate to organizational risk tolerance; it does not mandate a particular set of employee-use tiers. An organization may find it practical to define local levels such as routine assistance, sensitive work requiring approval, and prohibited or high-impact use. Those labels are an implementation choice, not a NIST classification.
For each proposed use, consider the following factors together:
- Input data: Does the prompt or connected source contain confidential, personal, customer, employee, or regulated information?
- Potential impact: Could an error or biased output affect employment, access to services, finances, safety, legal rights, or another consequential outcome?
- Audience and reliance: Will someone outside the organization see the output, or will a person rely on it without an appropriately qualified reviewer?
- Human oversight: Who checks the work, can they challenge or override the output, and do they have the expertise and time to do so?
- System access: Is the AI tool connected to internal files, code, email, or other organizational systems, and what can it access or change?
- Provider terms and safeguards: How are inputs retained or used, and what does the provider disclose about security, transparency, and relevant controls?
- Operational readiness: Can the organization monitor the use and respond if the system produces harmful output or data is exposed?
Use stronger approval, testing, documentation, and human review where the answers point to greater impact or exposure. A low-impact drafting task may need only an approved tool and an output check. A use involving sensitive information, external commitments, or consequential decisions may need prior review, limits on data and access, validation, and a named accountable human decision-maker. The precise controls depend on the organization and use case; no single tier label substitutes for that assessment.
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Do not treat fluent or confident wording as evidence that an answer is correct. NIST’s Generative AI Profile identifies confabulation and other generative AI risks. Require verification suited to the consequence of an error—for example, checking factual claims, calculations, citations, code, or generated content before relying on or sharing it.
How should tools, vendors, and integrations be reviewed?
Include third-party services and AI features embedded in other products in the approval process. Before work use is allowed, review the intended purpose and data flows as well as the provider’s relevant terms and safeguards. NIST’s Generative AI Profile discusses due diligence and third-party controls, including procurement review, service-level agreements, and assurance materials.
- Identify what information employees will provide and what sources or systems the service can access.
- Review retention and deletion terms, including whether inputs may be used in ways that conflict with organizational requirements.
- Assess security controls, access management, provider transparency, and how the provider handles relevant incidents.
- Consider intellectual-property concerns and contractual commitments for the intended work.
- Record the approved purpose, users, data boundaries, integrations, review owner, and any conditions employees must follow.
- Define how access will be disabled and data or integrations handled if the provider, use, or risk changes.
Approval of a vendor does not automatically approve every use of its service. A tool suitable for one data category or workflow may not be suitable for another; record material use cases and reassess them when their scope changes.
How should employees and managers be trained?
Train employees on decisions they actually face: which tools are approved, what data they may enter, how to verify outputs, when to disclose AI assistance, and how to report uncertainty or incidents. Use examples from the organization’s work rather than relying only on abstract principles. Give managers, approvers, and reviewers additional guidance matched to their responsibilities. NIST’s AI RMF and Generative AI Profile call for training appropriate to personnel roles and responsibilities.
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Make the reporting route easy to find and explain what information to provide, such as the tool used, the task, the data involved, and what happened. Employees should know whom to contact promptly if they suspect sensitive information was exposed or an output caused harm. The organization should route reports to the teams responsible for incident response rather than expecting employees to determine the technical or legal significance themselves.
How should the organization monitor and revise its rules?
Set a review owner and a review schedule, and revisit the policy after material incidents, new integrations, or changes in applicable requirements. Check whether employees understand the rules, whether the inventory remains accurate, and whether controls are preventing or detecting the risks identified in assessments. NIST’s Govern guidance emphasizes ongoing monitoring and periodic review, with roles and review frequency defined.
Reassessment matters when a use expands to new data, new users, external distribution, or a different decision context. It also matters when a service changes its terms or capabilities. Keep a record of material changes, approvals, and decisions so that the organization can explain how a use was governed and determine whether it should continue.
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What does an official governance example show?
The U.S. Equal Employment Opportunity Commission’s September 20, 2024 compliance plan describes agency leadership issuing a communication to employees and contractors on March 21, 2024, about generative AI risks and existing technology policies. The plan also describes an AI evaluation process and attention to staff expertise and professional development. It is an example of one agency’s internal governance work, not a legal template or a requirement that applies to all employers.
What NIST guidance can inform the approach?
NIST’s AI RMF is voluntary. NIST released its Generative AI Profile on July 26, 2024, and its framework page states that AI RMF 1.0 is being revised. NIST’s Govern guidance treats governance as a lifecycle-wide function, including compliance considerations across the other AI RMF functions. These materials can inform an organization’s policy design, but they do not determine the legal obligations of a particular employer, jurisdiction, or high-impact use. Obtain jurisdiction- and use-specific legal review where needed.
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