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A useful policy is specific enough to guide an ordinary decision: which tool may be used, with what data, for which purpose, who checks the result, what must be disclosed and what happens when something goes wrong. Use a risk-based framework rather than an absolute ban, then review it whenever a tool, law or use case changes.
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Begin with the setting, not a generic AI rule
AI governance is contextual. The National Institute of Standards and Technology (NIST) describes its AI Risk Management Framework (AI RMF) as voluntary and intended to help organizations incorporate trustworthiness into the design, development, use and evaluation of AI systems. NIST also says the framework is being revised, so check the current edition when adopting it.
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Use the policy as an operating document, not a statement of optimism or fear. NIST’s Govern Playbook recommends recording intended purposes, roles, data practices, testing, review, monitoring, change management, stakeholder engagement and incident response, including when an AI system is supplied by a third party.
Map the uses and assign risk tiers
Create an inventory before deciding what is allowed. Include standalone generative tools, AI features embedded in office, customer-service, security or moderation software, models accessed through an API, and any automated scoring or recommendation system. For each use, record the purpose, users, data, affected people, output, reviewer and consequence of an error.
Low-risk assistance
Low-risk uses support a person without making or materially driving a decision about another person. Examples include brainstorming headings, converting an employee’s own notes into a draft, translating a non-sensitive announcement, or suggesting neutral formatting. The user remains responsible for checking accuracy, confidentiality and licensing before using the result.
Restricted uses
Restricted uses can affect rights, opportunities, reputation, safety, money or access. Examples include drafting performance feedback, summarizing customer complaints, generating public statements, recommending content removals, producing security instructions, or analyzing employment applications. Require an approved tool, appropriate data handling, documented human review and an escalation path.
Prohibited or separately approved uses
Prohibit a use when the organization cannot provide lawful authority, adequate security, meaningful human oversight or a reliable way to correct harm. A separate approval process may be more appropriate than a permanent ban for high-impact cases that need specialist review. Examples include making a hiring, firing, promotion, pay, admission, benefits, account-closure or safety decision solely from an automated output; entering confidential information into a tool that is not approved for it; or using AI to impersonate a person without authorization.
| Tier | Typical purpose | Minimum control |
|---|---|---|
| Low risk | Drafting, brainstorming or formatting where a person owns the final work | Use an approved tool; check accuracy, confidentiality and rights before use |
| Restricted | Outputs that influence a person, public communication, moderation or a sensitive business process | Approved tool, documented purpose, data controls, qualified human review and escalation |
| Prohibited or separately approved | Solely automated consequential decisions, unapproved sensitive-data processing or impersonation | Do not use without a documented exception, specialist approval and controls matched to the harm |
Do not label an entire product “safe” or “unsafe” without describing the use. A single service may be acceptable for public text and unacceptable for confidential case files. Federal agency guidance illustrates this approach by supporting limits on particular services or uses according to risk rather than requiring a universal ban; those examples govern federal agencies, not every employer or community.
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Write a precise scope
Open the policy with definitions that ordinary users can understand. Define “AI system,” “generative AI,” “automated decision,” “personal information,” “confidential information,” “approved tool,” “human review” and “community moderator” as they apply to your organization. State whether the rules cover employees, contractors, interns, volunteers, moderators, vendors and members.
Name the activities covered: internal work, customer or member support, recruitment, publishing, moderation, research, coding, security operations and appeals. Include AI embedded in software that users may not recognize as an AI product. State whether personal experimentation on an organizational device or account is covered, and identify the owner who can approve an exception.
Scope should also identify the policy’s limits. A workplace rule may apply to work systems and work product while a community rule applies to member submissions and platform enforcement. If the same organization operates both, publish two audience-specific rules that share definitions but do not merge employee discipline with member moderation.
Make sensitive data a hard boundary
List the information users may enter, may enter only into a named approved environment, and may never enter. Align the list with existing data-classification and retention rules rather than creating a conflicting taxonomy.
| Information category | Policy treatment | Required user action |
|---|---|---|
| Public information | May be used in an approved tool when the purpose is permitted | Check the output for accuracy, confidentiality and rights |
| Internal operational information | Use only tools approved for the organization’s internal data | Confirm account settings, retention and access controls before submission |
| Personal, confidential, proprietary or regulated information | Restricted to explicitly approved systems and purposes | Minimize the data, document the purpose and follow privacy and security procedures |
| Secrets, credentials and highly sensitive case material | Do not enter unless a documented control specifically authorizes it | Use the incident process if disclosure occurs |
Explain what “approved” means. Approval should cover the account or tenant, training and access controls, vendor terms, retention, model-improvement settings, logging, deletion and geographic processing where those factors matter. Do not assume that a paid account, an enterprise label or a familiar brand automatically makes a use appropriate.
Give users a short accidental-disclosure procedure: stop further sharing, preserve the relevant facts without copying more sensitive data, notify the named security or privacy contact, follow containment instructions and record who may have received the information. The policy should protect good-faith reporting rather than encouraging concealment.
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Federal guidance emphasizes protecting government information, and California’s 2025 state policy prohibits entering confidential, proprietary or sensitive state information into commercially available generative AI. Those are government-specific examples of a data boundary, not a rule that automatically applies to a private employer or an online community.
Keep a responsible human in consequential decisions
“Human in the loop” is meaningful only when the person can understand enough of the output, check relevant evidence, correct errors and reject the recommendation. Name the responsible role, the review point and the record that must be kept. The reviewer needs suitable expertise, sufficient time and authority to stop or change the action.
Set a stronger standard for high-impact uses
For hiring, promotion, discipline, access, benefits, safety, account suspension or other consequential outcomes, require a human to review the underlying facts rather than merely clicking approval. Record the material reasons for the decision, the AI output’s role, any disagreement and the appeal or correction route.
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New York State Office of Information Technology Services guidance NYS-G25-002, issued in June 2026, says that use of automated decision-making systems by covered state agencies must be subject to human review. California’s 2025 state policy similarly requires human verification of generative-AI output used for decisions. These controls illustrate policy design for those state agencies; they are not universal requirements for every organization.
Check for discrimination and accessibility
The EEOC warned in 2021 that AI tools may “mask and perpetuate bias or create new discriminatory barriers to jobs.” If AI influences employment decisions, add a fairness review appropriate to the use, preserve records needed to investigate a complaint, provide an accommodation route and consult current employment and civil-rights law. Do not treat a vendor’s accuracy claim as proof that a decision is nondiscriminatory.
Define disclosure and records
Specify when a user must disclose AI assistance internally, to a customer, to a job applicant, to a member or to the public. Disclosure may be required when AI materially shaped a published work, a moderation action or a decision affecting a person. State what logs, prompts, source material, reviewer notes and final decisions must be retained and for how long under applicable records rules.
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Workplace rules: control work, data and employment decisions
A workplace policy should answer four practical questions: which tools employees may use, what information they may provide, what work requires verification, and who owns the final decision or work product.
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- Brainstorming, outlining or reformatting non-sensitive material.
- Drafting code, documentation or internal communications when a qualified employee tests and reviews the result.
- Translating or simplifying public information after checking meaning and confidentiality.
- Summarizing an employee’s own non-sensitive notes when the approved environment supports the data.
Workplace uses that need restriction
- Recruiting, screening, performance assessment, scheduling, pay, promotion, discipline or termination.
- Customer, patient, student or employee records, confidential contracts, trade secrets, credentials and security incidents.
- External statements, legal or regulatory submissions, financial forecasts and safety instructions.
- Automated monitoring that could affect an employee’s rights, opportunities or working conditions.
Separate ownership from authorship. An employee may use AI to produce a first draft, but the organization should identify who verifies facts, who approves release and who is accountable for errors. Clarify treatment of confidential source material, retained prompts, generated code and third-party content under existing contracts and intellectual-property rules; obtain qualified legal advice where those rules are uncertain.
Implementation sequence for an employer
- Inventory. Ask each department to list current and proposed AI uses, embedded features, data types, affected people and business owners.
- Classify. Put each use into a low-risk, restricted or prohibited/separately approved tier using the consequence of error, sensitivity of data and degree of automation.
- Approve tools. Record allowed accounts, settings, retention, access, vendor terms and technical restrictions. Block or limit unapproved services where appropriate.
- Assign review. Name the human role, evidence to check, approval record and escalation route for every restricted use.
- Train. Teach approved workflows, data handling, limitations, bias and accessibility concerns, disclosure, records and incident reporting.
- Monitor and revise. Review logs, complaints, incidents, model or vendor changes and new legal requirements on a stated schedule.
Online-community rules: govern content, moderation and appeals
A community policy should tell members what AI assistance is acceptable and tell moderators how automated systems may be used. It should also explain how a person can understand and challenge an enforcement decision.
Member-created content
Decide whether members may use AI to brainstorm, translate, edit or generate posts, images, audio or code. If disclosure matters to the community’s purpose, require a clear label when AI materially generated the submission, not merely when it corrected spelling. Prohibit impersonation, deceptive synthetic evidence, unauthorized personal-data processing and content that violates the community’s existing safety or intellectual-property rules.
Automated moderation
Document what the system detects, whether it blocks, labels, ranks or recommends content, and when a moderator must review it. Set stricter review for permanent removal, account suspension, safety reports and other actions with serious consequences. Preserve enough context for a reviewer to assess language, cultural meaning, satire, accessibility and possible model error.
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Tell members when AI assists moderation, what kind of signal it provides and who makes the final decision. Provide an accessible notice, a way to appeal, a response timeframe and a route for urgent safety concerns. Explain consistent factors used in enforcement without publishing details that would enable evasion. Moderators should document the reason for the action and correct an error promptly.
UNESCO’s guidance on digital-platform governance places transparency, accountability, accessibility, human-rights due diligence and stakeholder involvement at the center. It offers principles rather than a ready-made AI community policy, so adapt them to the community’s size, language, risks and legal setting.
Community implementation sequence
- Identify participants. Include members, moderators, platform operators, trust-and-safety staff and affected groups when drafting rules.
- Define labels and prohibited conduct. Use examples members can recognize, including impersonation, synthetic evidence and undisclosed material generation where relevant.
- Set moderation controls. State which actions may be automated, which require human confirmation and which require a second reviewer.
- Publish process information. Describe notices, reasons, appeals, accessibility support and urgent escalation in plain language.
- Audit outcomes. Review false positives, unequal impacts, appeal reversals, language coverage and changes in model behavior.
When one organization has both employees and a community
Keep the internal employee policy and the member-facing community policy separate. Employees may handle confidential records and have employment obligations; members submit content and need clear notice and appeals. A moderator’s authority over a post is not the same as a manager’s authority over an employee, and the evidence, records and consequences differ.
| Policy dimension | Workplace | Online community |
|---|---|---|
| Who is covered | Employees, contractors, managers, system owners and reviewers | Members, moderators, administrators and platform operators |
| Primary controls | Approved tools, data handling, work-product review and employment decisions | AI-assisted content, labeling, automated moderation, notices and appeals |
| Accountable role | Manager, process owner or qualified human decision-maker | Moderator, community administrator or platform governance owner |
| Evidence of fairness | Decision records, review notes, accommodation and discrimination checks | Consistent enforcement, accessible explanations, appeal outcomes and stakeholder input |
Share a central incident channel, definitions, training principles and change-management process, but do not publish internal security details in member rules. Ensure each audience knows which policy applies and where to report a concern.
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Communicate, train and maintain the boundary
Make the rule usable at the moment of choice
Put a short decision aid beside approved tools: “What is the purpose? What data is involved? Could an error affect a person? Who reviews the result? Must I disclose AI assistance?” Link it to the full policy, approval form and incident contact. Use examples from real workflows rather than abstract warnings.
Assign ownership and review dates
Name a policy owner with authority to approve changes and a cross-functional group for privacy, security, legal, accessibility, human resources or community safety as appropriate. Review on a defined cadence and whenever a model, vendor, data flow, audience or law changes. NIST’s Govern Playbook specifically calls for training, monitoring, auditing, change management and incident response.
Test the policy
Run tabletop exercises for an accidental disclosure, a biased employment recommendation, a hallucinated public statement, a mass false-positive moderation event and an appeal that reveals a model error. Check whether users know whom to contact, whether reviewers can obtain the underlying evidence and whether the organization can pause the system.
A concise policy blueprint
The following headings can be adapted to either context without pretending that one template satisfies every legal obligation.
Purpose and scope: covered tools, people, activities and definitions.
Risk tiers: allowed, restricted and prohibited or separately approved uses.
Approved tools: accounts, settings, data limits, retention and access controls.
Data rules: permitted information, forbidden information and disclosure response.
Human review: responsible role, evidence to check, approval record and escalation.
Transparency: when AI use, labels, notices or explanations are required.
Fairness and accessibility: testing, accommodations, language and impact checks.
Records: prompts, source material, outputs, decisions, appeals and retention.
Incident response: reporting channel, containment, investigation and correction.
Training: required instruction, refresh schedule and acknowledgements.
Governance: policy owner, exception authority, audit method and review cadence.Before adoption, have qualified privacy, security, employment, accessibility and content-governance advisers check the sections relevant to your jurisdiction and use case. The exact legal requirements cannot be determined without those facts.
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