How do we set responsible AI policies for employees? Start by identifying the AI tools and uses already present across the organization, then approve use cases according to their context and potential impact. Give employees practical rules for approved tools, data handling, human review, and escalation—and assign named owners to maintain those rules. An employee policy works best as one part of a governance system connected to privacy, security, legal, HR, accessibility, procurement, and records processes.
This is a governance baseline, not a determination of what any particular employer is legally required or permitted to do. Requirements depend on jurisdiction, sector, the people affected, and the specific use.
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What should an employee AI policy cover?
A useful policy tells employees what they may do and gives the organization a way to assess and manage AI use over time. It should cover employees and contractors, organizational work, AI features embedded in existing products, and separately acquired tools. Define what counts as AI for the policy’s purposes, who owns it, and how exceptions and changes are handled.
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Name decision owners
Assign accountable people or functions for policy ownership, tool approval, use-case risk review, employee training, incident response, exceptions, and periodic review. Different roles may belong to different teams, but an employee should know where a decision goes and who can make it. Record who uses, oversees, and is affected by a system, along with the responsibilities attached to those roles.
NIST’s AI Risk Management Framework (AI RMF) treats governance as continuous across an AI system’s lifespan and the organization. Its Playbook suggests defining roles, oversight responsibilities, and risk-management training protocols. The Playbook is voluntary guidance, not a required process or compliance certification.
How do we find the AI tools and uses already in the organization?
Build an inventory before expanding access or assuming that AI use is confined to tools explicitly marketed as AI. Existing business software may include AI features, and employees may seek out separate services. Ask business teams, IT, security, legal, procurement, and HR to identify both kinds of use.
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For each tool or proposed use case, record:
- The tool or vendor, business owner, purpose, users, and procurement or approval status.
- The information it receives and produces, including whether personal, confidential, regulated, customer, or source-code data is involved.
- Who may be affected, what decision or work product the AI supports, and how consequential an error could be.
- Whether a qualified person reviews the output, what that person can inspect or change, and how errors can be corrected.
- How the use will be monitored and what changes would trigger a new review.
Give employees a clear intake route—such as a designated request form or service desk category—for proposing a tool, enabling an AI feature, or using an existing tool for a materially different purpose. The route should gather enough information for the relevant owners to assess the use, without asking employees to make a legal or technical risk determination on their own.
The EEOC’s September 20, 2024 Compliance Plan for OMB Memorandum M-24-10 describes reviewing an agency software inventory for AI elements and developing an AI questionnaire for IT and acquisition assessments. It is an example of a government agency’s process, not a mandate for private employers.
When does an employee need approval to use AI at work?
Set approval requirements by use case, not just by tool brand. The same tool could be used for a low-impact drafting task or to support a consequential decision; those uses warrant different review. Require advance review before broad access, a material expansion of use, or use that could affect employment, customers, access to services, safety, or other important outcomes.
The following routing model is an example for policy design, not a universal risk scale or a set of legal thresholds:
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|---|---|---|
| Reformatting an internal meeting agenda using non-sensitive notes in an approved tool | Permit under ordinary employee rules, with output checking appropriate to the task | The use is administrative and does not, by itself, determine an important outcome. |
| Summarizing customer correspondence or generating code for a business system | Use only in an approved environment and follow the relevant data, security, and verification rules; seek review if the purpose or information is outside the approved scope | Customer information, code, or downstream use may raise privacy, security, reliability, or contractual concerns. |
| Ranking job applicants, recommending employee discipline, or influencing access to a service | Require advance review by appropriate domain owners, including legal, privacy, security, accessibility, and HR where relevant | Errors or bias may affect people’s rights, opportunities, or access to important services. |
| Using AI in a safety-related or otherwise high-consequence operational decision | Require documented risk review and defined human authority before use; do not proceed until accountable owners approve the use | Potential harm, limits on oversight, and the ability to detect and correct errors need assessment in the specific setting. |
For each proposal, reviewers should consider affected people, severity and likelihood of errors, data sensitivity, reliability, security, privacy, fairness, transparency, accessibility, oversight, and whether the organization can monitor and correct outcomes. Involve people with different relevant perspectives; no single generic checklist can settle every use case.
What can employees put into AI tools?
State the rule in terms employees can apply: use work information only in tools approved for that information and purpose. Then map the rule to the organization’s data classifications, contracts, security controls, and applicable requirements. Do not assume every service has the same data retention, access, or reuse terms.
Unless the organization has explicitly approved a tool and use for a data category, employees should not enter confidential business information, personal or regulated data, customer records, source code, credentials, or other restricted material. Provide a route to ask whether a specific category is permitted; do not leave employees to infer permission from the fact that a tool is available.
Approval of a tool does not automatically authorize every kind of information or every purpose. Keep the approved-tool list tied to permitted uses and data categories, and tell employees where to find current guidance.
When must employees verify or document AI output?
Require verification before an employee relies on AI-generated facts, calculations, citations, code, summaries, or recommendations. The depth of checking should match the consequences of an error. A citation should be checked against the cited source; code should be tested and reviewed under the organization’s normal development controls; and a recommendation affecting a person should be assessed against relevant evidence rather than accepted because a system produced it.
For consequential decisions, identify a qualified person who can inspect relevant information, question the output, correct it, and make or approve the final decision. A signature or click-through is not meaningful oversight if the reviewer lacks the information, competence, authority, or time to challenge the recommendation.
Specify when employees must disclose or document AI assistance, such as when required by an organizational process, contract, or applicable rule. Define where the record belongs and what it should capture—for example, the approved tool and use, the human reviewer, and material corrections—without collecting unnecessary personal or sensitive information.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should the organization train employees and handle incidents?
Train for the decisions employees actually make
Training should explain the approved tools and uses, data restrictions, output verification, security practices, bias and accessibility concerns, disclosure or documentation rules, and how to request approval or report a concern. Tailor guidance for employees who procure, configure, oversee, or rely on AI, as well as those who use it for routine work.
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Make reporting and escalation specific
Tell employees how to report an incorrect or harmful output, suspected bias, unexpected exposure of information, security concern, or unapproved use. Identify a contact channel and explain what information to provide, such as the tool, use case, date, affected workflow, and steps already taken. Employees should know whether to stop using the system or preserve relevant records while the appropriate incident owner assesses the issue.
Route reports to the right response function—such as security, privacy, legal, HR, or the business owner—based on the incident. The organization should define who can pause a use, notify affected internal teams, assess the impact, and authorize a return to service.
How should CIOs use NIST guidance?
NIST’s AI RMF 1.0, released January 26, 2023, is a voluntary, rights-preserving, non-sector-specific, use-case-agnostic framework. NIST’s AI RMF page says version 1.0 is being revised; do not treat it as law or a certification, or assume it is the final revision. NIST’s Generative AI Profile, NIST AI 600-1, released July 26, 2024, is a cross-sector companion to AI RMF 1.0 with lifecycle-oriented actions for managing generative AI risk in line with organizational goals and priorities.
The AI RMF Playbook groups suggested actions under four functions: Govern, Map, Measure, and Manage. It explicitly says it is “neither a checklist nor set of steps to be followed in its entirety” and that its suggestions are voluntary. Use those resources to structure discussion and identify gaps, selecting actions that fit the organization’s context, risk tolerance, and resources rather than presenting framework adoption as proof that every risk is controlled.
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Assign an owner and a defined review cadence. Revisit the policy when tools, data flows, uses, affected populations, or relevant requirements materially change, and use incidents, complaints, quality issues, and employee questions to identify where rules need clarification. Reassess a use before expanding it into a new workflow or relying on its output for a more consequential decision.
The EEOC’s 2024 plan says its agency AI use-case inventory and evaluation process is to be reviewed and updated on an ongoing basis and at least every two years. That cadence describes the agency’s stated practice; it is not a general requirement for employers. Organizations should set a review schedule that fits their own risks and obligations.
What depends on the organization’s circumstances?
This guide cannot determine whether a particular employee use is lawful or permissible for an organization whose locations, sector, workforce, contracts, and intended AI uses are unspecified. Before approving consequential uses or asserting legal compliance, the relevant legal and control owners should map applicable requirements to the actual use case and people affected. NIST’s Playbook likewise notes that legal requirements differ across applications and contexts.
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