An AI-agent policy employees can follow is a short, operational set of rules that answers six questions before anyone deploys an agent: which tools and tasks are approved, what data may enter them, which systems and actions they may access, when a person must review the result, how incidents are reported, and who updates the rules. Build the policy around risk rather than around a single product. A read-only agent summarizing public documents needs different controls from an agent that can edit customer records, send messages, approve spending, or influence employment decisions.
NIST’s AI Risk Management Framework (AI RMF) is a useful voluntary organizing framework, not a law and not a ready-made employee handbook. Its four functions—Govern, Map, Measure, and Manage—help an employer assign ownership, understand use cases, test risks, and apply controls. NIST says the framework is intended to help developers, users, and evaluators manage AI risks that could affect individuals, organizations, society, or the environment. NIST is revising the framework, so confirm the current edition before formally adopting it.
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What an employee-facing AI-agent policy must accomplish
A usable policy turns broad principles into decisions an employee can make at the moment of use. It should tell a person whether a proposed tool and task are allowed, what information can be supplied, whether the agent may connect to a system, what approval is needed before it acts, how to check its output, and where to report a problem.
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Start with a risk-based rule
Use four questions to set the strictness of a rule:
- Impact: Is the task routine drafting or summarization, or could it affect safety, rights, money, employment, access to services, or reputation?
- Data sensitivity: Does the agent handle public material, internal information, personal data, confidential business information, regulated records, or contractual secrets?
- Autonomy and access: Is the agent read-only, or can it write to systems, send messages, change permissions, approve work, or initiate a transaction?
- Reversibility and oversight: Can a person easily inspect and undo the result, or would an error be difficult to detect or reverse?
Worker impact is an additional test. Uses involving hiring, evaluation, discipline, compensation, workplace monitoring, workload, or job quality require legal and HR review before deployment.
Define scope, approved tools, and permitted work
Publish an inventory of approved tools and configurations rather than a vague statement that employees may use “company AI.” Name the service, approved account type, enabled features, connected systems, data restrictions, and owner. Consumer accounts, enterprise accounts, plug-ins, browser extensions, and locally installed models may have different retention and access settings; approval for one does not automatically approve the others.
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Maintain a register that employees can search. Each entry should include:
- Tool and version or configuration, including whether browsing, file upload, memory, code execution, or external connectors are enabled.
- Business owner and technical administrator.
- Approved tasks and prohibited tasks.
- Permitted data classes and retention requirements.
- Systems the agent may read or change.
- Required human approval points and logging.
- Review date, change history, and route for exceptions.
When an employee wants a new tool or use case, require a short intake covering the task, data, affected people, connected systems, expected benefit, failure modes, and reversibility. Security, privacy, procurement, legal, HR, or records-management reviewers should be involved when the answers indicate elevated risk. Do not let an employee bypass review by calling the same service through an unapproved interface.
Separate permitted, restricted, and prohibited uses
| Use category | Typical examples | Default policy treatment |
|---|---|---|
| Routine assistance | Summarizing public material, brainstorming, formatting, creating a first draft from non-sensitive notes | Permit only in approved tools; employee checks accuracy and removes unsupported claims. |
| Internal operational support | Searching internal procedures, classifying tickets, drafting an internal report | Require approved data source, least-privilege access, logs, and review before distribution. |
| External or consequential action | Sending customer or regulator communications, changing a record, approving a payment, publishing content | Require named human approval immediately before execution and a recoverable audit trail. |
| High-impact people decisions | Hiring, promotion, discipline, compensation, monitoring, scheduling that materially affects workers | Do not deploy without legal and HR review, documented human decision authority, bias and accuracy testing, and worker-impact controls. |
Mark prohibited uses plainly. Examples include entering restricted data into a tool that has not been approved for that data, allowing an agent to make an unreviewed decision about a person’s employment or access to a critical service, and granting broad write access when a read-only workflow would meet the need.
Set data-handling rules employees can apply
Data rules must connect the agent to the organization’s existing privacy, confidentiality, security, records-retention, and contractual obligations. A vendor’s marketing description is not proof of how prompts, files, logs, feedback, or outputs are stored or used. Review the service terms, administrator settings, subprocessors, retention controls, regional storage, access model, and deletion process before approval.
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| Data class | Employee may enter it when | Required safeguards |
|---|---|---|
| Public information | The source is already public and no confidentiality duty applies. | Use an approved account; check the output for fabricated facts and copied material. |
| Internal, low-sensitivity information | The tool is approved for internal data and access is limited to the employee’s work. | Use company authentication, avoid unnecessary personal details, and follow retention rules. |
| Confidential or proprietary information | The tool’s contract and settings expressly permit the data and the use has an approved business purpose. | Apply least privilege, limit the prompt to necessary fields, prevent training or secondary use where required, and retain an audit record. |
| Personal, regulated, or specially protected information | Privacy, security, legal, and records owners have approved the specific use and jurisdiction. | Use approved safeguards, access controls, minimization, retention and deletion procedures, and documented review. |
| Restricted secrets or data barred by contract | Only if the responsible owner has explicitly authorized the exact workflow; otherwise never. | Keep it out of the agent, including pasted excerpts, attachments, screenshots, and retrieved context. |
Teach employees that indirect disclosure is still disclosure. Names can be removed while dates, job titles, account numbers, or distinctive facts leave a person identifiable. An agent connected to a document repository may expose more information through retrieval than the employee intended to paste. Prohibit uploading whole folders when a narrow excerpt or a structured, minimized field set is sufficient.
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Control retention and records
Decide whether prompts, tool calls, outputs, approvals, and agent-generated records are business records. Specify who may access logs, how long they are retained, and how a legal hold or deletion request affects them. If an agent drafts a record that becomes part of an official file, the employee must verify it and store the final approved version in the designated system rather than relying on an agent workspace.
Control permissions, tools, and external actions
Give an agent the smallest set of permissions needed for the approved task. Read-only access is safer than write access; a narrowly scoped API is safer than an administrator credential; a draft queue is safer than direct sending. Separate the agent’s identity from the employee’s personal credentials so actions can be attributed and revoked.
Use an autonomy ladder
| Level | What the agent can do | Minimum control |
|---|---|---|
| 1. Suggest | Generate text, code, classifications, or plans without system access. | Employee checks material facts, sources, confidentiality, and suitability. |
| 2. Retrieve | Read approved repositories or APIs. | Least-privilege, source scoping, access logging, and protection against cross-user data exposure. |
| 3. Prepare | Create drafts, tickets, forms, or proposed record changes. | Human review of the complete proposed action before submission. |
| 4. Execute with confirmation | Send, update, or trigger an action after an explicit approval. | Show the exact payload, destination, and consequences; require an identified approver and preserve the approval log. |
| 5. Limited autonomous execution | Perform pre-approved low-impact actions within strict boundaries. | Technical guardrails, transaction limits, allow-lists, monitoring, rapid stop controls, periodic sampling, and a named owner. |
Do not permit autonomous execution for consequential or difficult-to-reverse actions unless a documented risk assessment, tested controls, and accountable owner justify it. External communications, changes to important records, access-control changes, payments, legal submissions, safety-related actions, and employment decisions should normally require human confirmation.
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Design the approval screen and break-glass process
Before approval, show the employee what the agent will do, which data it will transmit, which account it will use, the recipients or records affected, and whether the action can be undone. “Approve all future actions” is not an adequate substitute for a review point on consequential work.
If an urgent situation requires bypassing a normal control, use a break-glass process with a reason, named approver, time-limited access, enhanced logging, and a retrospective review. Emergency status must not become a routine shortcut.
Require human review and output checks
Human review is not a ceremonial click. The reviewer must have enough context, authority, time, and technical visibility to reject or correct the result. Assign review intensity to the consequences of being wrong.
Match review to the output
- Low consequence: Check that the answer addresses the request, contains no obvious fabricated facts, and does not reveal restricted information.
- Material business content: Verify names, dates, figures, quotations, citations, calculations, source coverage, and policy interpretation against authoritative records.
- Safety, legal, financial, rights, or reputation impact: Require a qualified subject-matter reviewer, independent source checks, documented approval, and a route to escalate uncertainty.
- People-related decisions: Do not treat an agent score or recommendation as the decision. A responsible human must assess relevant evidence, explain the decision, and follow applicable law and organizational procedure.
Give employees a check-before-use checklist
- Confirm that the tool and use case are on the approval register.
- Remove data that is not necessary for the task and confirm the remaining data is allowed.
- Check every material factual claim against a reliable source; recalculate important numbers independently.
- Look for invented citations, missing context, biased or discriminatory assumptions, unsafe instructions, copied language, and prompt-injection content from retrieved documents.
- Verify that the output follows the organization’s policy, contract, accessibility, records, and confidentiality requirements.
- Record the reviewer, approval, source checks, and any material edits when the output enters an official workflow.
For code, require testing in a safe environment, dependency and license checks, security review, and a human owner before production deployment. For generated images, audio, or text, check rights, consent, attribution, impersonation risk, and whether the audience could mistake synthetic material for an authentic statement.
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Test an agent before approval and after material changes to its model, prompts, connectors, permissions, or data. Include normal cases and adversarial cases: ambiguous instructions, conflicting sources, malicious documents, unauthorized requests, unavailable systems, and attempts to retrieve another user’s information.
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NIST identifies trustworthiness considerations including validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy, and fairness with harmful bias managed. Its FAQ says these considerations apply across pre-design, design and development, deployment, use, and test and evaluation. Use them as a review lens rather than as a promise that every risk can be eliminated.
Monitor what matters
- Authentication, permission changes, tool calls, retrieved sources, approvals, outputs, errors, and reversals.
- Unusual volume, repeated failed actions, attempts to reach blocked systems, or access outside a user’s role.
- Accuracy complaints, harmful content, privacy exposure, security events, and unexplained changes in behavior.
- Whether workers are being monitored or evaluated in ways not disclosed in the policy or required consultation.
NIST is developing security-control overlays for single-agent and multi-agent systems. That work supports treating agent permissions and system security as explicit policy topics; it is not a finished mandatory control standard. Until applicable controls are finalized, use established security practices such as segmentation, secrets management, allow-lists, rate limits, sandboxing, dependency updates, and tested rollback procedures.
Define incident reporting and recovery
Employees need a single, visible route for suspected harmful outputs, unauthorized data exposure, unintended actions, security weaknesses, or discriminatory effects. Tell them to stop the agent or disconnect the affected integration when safe, preserve relevant prompts and logs, and report promptly rather than quietly correcting evidence.
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Minimum incident workflow
- Contain: Pause the agent, revoke a token, disable a connector, quarantine an output, or stop distribution as appropriate.
- Notify: Contact the designated security, privacy, legal, HR, safety, or system owner based on the incident type.
- Preserve: Save timestamps, prompts, retrieved sources, tool calls, approvals, outputs, recipients, and system state, subject to applicable privacy and retention rules.
- Assess: Determine who or what was affected, whether data left the approved boundary, whether an action can be reversed, and whether notification obligations may apply.
- Recover: Correct records, retract communications, restore systems, notify affected people when required, and remove unsafe configurations.
- Learn: Update the tool, prompt, permission, training, or policy; record the owner and due date.
Make clear that good-faith reporting is protected from retaliation. An employee should not need to prove that harm occurred before raising a credible concern.
Handle employment and worker data separately
Uses involving hiring, screening, promotion, discipline, compensation, scheduling, productivity measurement, workplace monitoring, or worker records require a specialized review. Accuracy, explainability, privacy, discrimination, accommodation, labor-relations, and job-quality concerns can arise even when an agent only recommends an action.
Obtain legal and HR review for the relevant jurisdiction and use case, document the accountable human decision-maker, test for disparate errors and inaccessible processes, and give affected workers an appropriate way to understand, challenge, or correct material information where required. Seek worker input when an agent changes how work is performed, measured, assigned, or monitored.
The U.S. Department of Labor’s October 16, 2024 announcement presented workplace AI best practices as a roadmap emphasizing worker input, empowerment, well-being, and job quality. Acting Secretary Julie Su said, “We have a shared responsibility to ensure that AI is used to expand equality, advance equity, develop opportunity and improve job quality.” The roadmap is guidance, not a universal private-employer requirement; check current materials and applicable law before relying on it.
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Assign governance, training, and revision duties
Name one policy owner with authority to approve changes, and assign supporting owners for security, privacy, records, legal, HR, procurement, and business operations. The owner should maintain the tool register, review incidents and metrics, coordinate worker consultation, and retire tools that no longer meet requirements.
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Train before access, then refresh
Training should be specific to the approved tools employees actually use. Cover data classifications, prohibited inputs, connector permissions, review obligations, fabricated content, prompt injection, copyright and confidentiality, incident reporting, and how to request a new use case. Give managers additional training on approving workflows and recognizing when an agent is being used for a people decision.
Refresh training when a tool gains new connectors or autonomous features, when a material incident occurs, and on a regular schedule set by the policy owner. Record completion and provide a short employee reference card with the approved-tool link, data rule, human-approval rule, and reporting channel.
Review the policy as capabilities change
Set a review trigger for a new model, connector, data source, autonomy level, material incident, legal change, contract change, or worker-impacting deployment. NIST’s Playbook groups suggested actions under Govern, Map, Measure, and Manage and expressly describes them as adaptable suggestions rather than a checklist that every organization must follow. Use the parts that fit your organization’s goals, legal context, and risk priorities.
A practical policy outline to publish
- Purpose and scope: Define agents, covered workers, systems, contractors, and business processes.
- Approved tools and uses: Link to the live register and explain the request path for additions.
- Data handling: State allowed, restricted, and prohibited data for each tool; incorporate privacy, security, confidentiality, retention, and contract rules.
- Permissions and actions: Specify read, write, send, and transaction limits; require least privilege and separate credentials.
- Human review: Identify actions that always require confirmation and the qualifications needed for high-impact review.
- Output standards: Require fact checking, source verification, bias and safety checks, rights review, and testing appropriate to the output.
- Incident reporting: Provide the channel, urgency levels, containment steps, evidence to preserve, and non-retaliation statement.
- Employment and worker impact: Route covered uses through legal and HR review and explain worker consultation and challenge processes.
- Training and accountability: Name owners, access prerequisites, manager duties, and consequences for bypassing controls.
- Revision and exceptions: Define review triggers, version control, expiration of exceptions, and approval authority.
Examples employees can use
Summarizing a public report
An employee may use an approved, read-only tool to summarize a public report. They should verify quotations, figures, and conclusions before publication. If the summary will support a financial, legal, safety, or public statement, a qualified reviewer checks it before release.
Drafting a customer reply from an internal ticket
The agent may retrieve only the ticket and approved knowledge articles, not the entire customer database. It may prepare a draft, but a trained employee confirms identity, account details, promised remedies, tone, and policy compliance before sending. Direct autonomous sending is not allowed unless the workflow has separately passed the organization’s consequential-action review.
Updating a business record
An agent can propose a change using a constrained form and show the source evidence. A human compares the proposal with the authoritative record, approves the exact fields, and retains the audit trail. Bulk updates require testing, sampling, rollback capability, and an owner who can stop the job.
Screening job applicants
Do not activate an applicant-ranking agent merely because it saves time. The use must go through legal and HR review, including jurisdiction-specific requirements, validation, accessibility, bias testing, disclosure or notice duties where applicable, and a human decision process that does not defer automatically to the model.
Legal and geographic boundaries
This approach is U.S.-oriented general guidance, not a legal conclusion for a particular employer. Requirements can vary by state, locality, country, industry, data type, collective-bargaining agreement, contract, and use case. The Equal Employment Opportunity Commission maintains AI governance resources and guidance that interprets laws enforced by the agency; consult the current materials and qualified counsel for an actual deployment.
Federal examples illustrate controls but do not automatically govern private employers. Executive Order 14110, published November 1, 2023, addressed federal workforce training and safeguards for government information. A Federal Trade Commission compliance plan published in September 2025 discusses agency governance of nonpublic data, accuracy, hallucinations, and plagiarism. Treat both as examples of public-sector risk management, not as a complete private-company policy.
Launch checklist for policy owners
- Inventory every agent, connector, account type, and autonomous capability already in use.
- Assign a business owner, technical owner, and policy owner to each approved workflow.
- Classify the data and document retention, access, and contractual restrictions.
- Apply least privilege; remove administrator credentials and unnecessary write access.
- Define the human approval point, reviewer qualifications, and rollback method.
- Test normal, erroneous, adversarial, privacy, security, and worker-impact scenarios.
- Enable logs that capture access, tool calls, approvals, outputs, and reversals without collecting unnecessary personal data.
- Publish the incident route and practice containment and recovery.
- Train employees and managers before granting access.
- Set review dates and triggers for capability, legal, incident, and worker-impact changes.
A policy succeeds when an employee can answer “Can I use this agent, with this data, for this action, and what must I check?” without guessing. Keep that decision path visible, enforce it through technical permissions, and revise it whenever the agent, the work, or the people affected by the work change.
Frequently Asked Questions
Is the NIST AI Risk Management Framework mandatory?
No. NIST describes the AI RMF as voluntary and adaptable. It can organize governance, mapping, measurement, and management activities, but it is not a universal employee policy or a substitute for applicable law and contracts.
Can an AI agent send emails or change records without approval?
Only when a separately approved workflow establishes that the action is low impact, tightly bounded, monitored, logged, and reversible. External communications, consequential changes, transactions, and difficult-to-reverse actions should normally require explicit human confirmation.
What should an employee do after an agent exposes confidential information?
Stop or disconnect the affected workflow when safe, preserve prompts and logs, and report immediately through the organization’s security or privacy incident channel. Do not quietly delete evidence or continue using the compromised configuration.
Does an enterprise subscription automatically make any data safe to enter?
No. The organization must review the specific contract, retention, training, access, connector, regional-storage, and deletion settings, then approve data classes and use cases individually.
Who should approve an AI agent used in hiring or performance management?
The workflow should undergo legal and HR review for the relevant jurisdiction, with security, privacy, and business owners involved as needed. A named human must retain decision authority, and the organization should test accuracy, accessibility, bias, privacy, and worker-impact risks.
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