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Set AI boundaries by defining the tools and activities covered, sorting uses into allowed, restricted, and prohibited tiers, protecting sensitive information, assigning a qualified human to review consequential output, and explaining how people can challenge decisions. Keep employee rules separate from member-facing community rules, then train users and review the policy whenever tools, data, or laws change.
What an AI-boundary policy must accomplish
A useful policy turns broad concerns about artificial intelligence into decisions people can make at the moment of use. It should answer five questions:
- Which tools and built-in AI features are covered?
- What work or community activities may use them?
- What information may be entered, and under which account or settings?
- When must a person check, edit, approve, or reject the output?
- How are mistakes, complaints, disclosures, and policy changes handled?
Do not treat one government or platform policy as a universal rule. 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 use-case profiles should reflect an organization’s setting, requirements, risk tolerance, and resources, and that an AI RMF revision is in progress. Use the framework as a way to structure decisions, not as a binding law.
NIST says the framework was developed over an 18-month period with more than 240 contributing organizations; those figures describe development of the framework, not the number of workplaces or communities with AI policies.
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Start with governance, scope, and ownership
Name a policy owner and decision rights
Give one role responsibility for maintaining the policy, while assigning operational duties to people who understand the relevant work. A workplace might designate an information-governance lead, security lead, human-resources representative, and business owner for each high-impact system. A community might assign an administrator, moderation lead, privacy contact, and appeal reviewer.
Write down who can approve a new tool, who can change a risk classification, who investigates an incident, and who can suspend a use. A policy without decision rights becomes advice that nobody is empowered to enforce.
Define “AI” broadly enough to catch embedded features
Cover standalone chatbots, image and audio generators, transcription and summarization services, coding assistants, recommendation engines, automated moderation, scoring systems, and AI features embedded in ordinary software. Users often do not recognize an embedded feature as a separate service, so the scope should name examples and say that substantially similar tools are included.
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Keep internal and public-facing rules distinct
If an organization runs a community, create two connected documents. Employee and contractor rules govern approved work tools, data handling, records, ownership, employment decisions, and escalation. Member rules govern submitted content, disclosure of AI assistance, automated moderation, enforcement explanations, and appeals. The principles can align, but the audiences, consequences, and legal duties differ.
Use risk tiers instead of a blanket ban
A tiered model makes low-risk assistance available while placing stronger controls around decisions that can harm a person, expose confidential information, or publish misleading material. The examples below are starting points; classify a use according to its actual data, audience, impact, and ability to reverse an error.
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| Tier | Typical uses | Minimum control |
|---|---|---|
| Allowed with ordinary review | Brainstorming, rewriting a non-confidential draft, generating placeholder code, summarizing material that is already public | Use an approved account, check for errors and unsupported claims, and remove private data before submission |
| Restricted | Customer communications, analysis containing internal information, code that will enter a production system, member-facing content, recommendations that affect access or visibility | Use only an approved tool and data setting; require a named reviewer; retain the source or decision record when appropriate |
| Prohibited unless separately authorized | Entering confidential, proprietary, or sensitive information into an unapproved commercial service; making an employment or access decision solely from an AI output; publishing unverified high-stakes advice as fact | Block the use by default; permit it only through documented approval, lawful authority, and controls that address the specific risk |
Federal agency guidance illustrates why risk-based access is often more workable than a universal ban: an agency can limit particular services or use cases according to risk. That is guidance for federal agencies, not a mandate for every employer or community.
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Classify information before anyone opens a tool
Map the organization’s existing data categories to AI use. State explicitly whether a category is public, internal, confidential, personal, regulated, security-sensitive, or subject to a contractual restriction. Users should not have to infer the answer from a vendor’s marketing page.
California’s 2025 state policy prohibits entering confidential, proprietary, or sensitive state information into commercially available generative-AI systems. That rule applies to California state operations; it is a useful example of a clear boundary, not a rule for every private employer or community.
Specify approved tools and settings
List the services that may be used for each data class and identify the account type, retention setting, administrator controls, and sharing options required. If a tool’s terms or settings change, pause restricted uses until the owner confirms that the tool still meets the policy.
Provide an accidental-disclosure path
Tell users exactly what to do if they paste protected information into the wrong service: stop further use, preserve the relevant details without spreading the data, notify the designated security or privacy contact, and follow the organization’s incident process. Include a separate route for community members who disclose personal information in a post or message. Reporting should be safe, fast, and available without requiring the reporter to diagnose the legal category.
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Make a human accountable for consequential output
“Human in the loop” is meaningful only when the person has the time, expertise, and authority to reject the result. Define the review action, not just the reviewer’s job title.
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Specify what the reviewer checks
- Factual accuracy and completeness against primary records or source material.
- Fabricated citations, quotations, statistics, or references.
- Privacy, confidentiality, security, and intellectual-property concerns.
- Discriminatory patterns, inaccessible language, or unequal treatment.
- Whether the output follows the organization’s policy and the person’s instructions.
Increase scrutiny when a decision affects a person
Require documented human verification before an AI-assisted result is used for hiring, promotion, discipline, dismissal, benefits, access, account sanctions, or other consequential outcomes. New York State Office of Information Technology Services guidance NYS-G25-002, issued in June 2026, states 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. Those requirements apply to the cited state agencies; the appropriate control for another organization depends on its jurisdiction, use case, and applicable law.
The reviewer should be able to correct or reject the result without treating the model’s score or explanation as presumptively correct. Preserve enough information to explain what the system produced, what evidence the reviewer considered, and why the final action was taken.
Account for discrimination risk in employment
The U.S. Equal Employment Opportunity Commission has warned that AI tools may mask or perpetuate bias or create new discriminatory barriers to jobs. A workplace policy should therefore require fairness review when AI influences recruitment, screening, evaluation, scheduling, promotion, discipline, or termination. Consult current EEOC guidance and appropriate legal or civil-rights expertise for the jurisdictions involved; the 2021 EEOC statement is not a complete summary of present legal obligations.
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Workplace boundaries: rules employees can follow
Permitted assistance
Allow low-risk drafting, formatting, translation, coding suggestions, meeting summaries, and brainstorming when the user removes protected data and independently checks the result. State whether the output may be copied directly into a customer message, report, codebase, or record, or whether an additional reviewer is required.
Work product, records, and ownership
Decide who owns and maintains an AI-assisted deliverable, where prompts and source files are stored, and which records must be retained. Require employees to preserve the underlying business record rather than relying on a chatbot transcript as the sole explanation of a decision. Clarify that using an AI tool does not transfer responsibility for accuracy, confidentiality, or compliance.
Employment and other high-impact decisions
Do not let an unreviewed model output determine a person’s employment status, pay, opportunity, access, or discipline. Require a qualified reviewer to examine relevant evidence, give the affected person any process the law or policy requires, and document the reason for the final decision. If the organization cannot explain or challenge the system’s output, it should not use that output as the decisive basis for the action.
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Escalation and exceptions
Provide a short route for questions such as “Is this data safe to enter?” and “Does this use require approval?” Exceptions should identify the business purpose, data involved, tool, duration, reviewer, safeguards, and expiration date. Avoid permanent exceptions that silently become the normal process.
Online-community boundaries: protect participation and legitimacy
Member-created content
Say whether members may use AI to draft posts, translate text, generate images, or edit audio. If disclosure matters to the community’s purpose, require a visible label or a short statement describing the assistance. Distinguish assistance from fully generated material where that difference affects trust, attribution, contests, educational work, or safety.
Automated moderation and ranking
Explain where automated systems assist moderators, filter spam, rank material, recommend content, or restrict accounts. A clear notice should state that automation may be used, what kinds of signals it considers at a high level, and whether a person reviews an appeal. Do not promise that every action receives identical review if staffing makes that impossible; instead, define which actions always receive human review and which can be reversed quickly.
Appeals and explanations
Give affected members an accessible way to request review, correct context, and report an erroneous or discriminatory result. Set a target response time, identify the reviewer or team, and explain what information the member should provide. Keep enforcement reasons specific enough to be useful without revealing private reports or security-sensitive detection methods.
UNESCO guidance on digital-platform governance places transparency, accountability, accessibility, human-rights due diligence, and stakeholder involvement at the center of platform governance. Those principles support clear community rules and appeal processes, but UNESCO does not prescribe a single AI-specific policy template.
Communicate, train, and maintain the rules
Publish rules where decisions happen
Put the policy in the employee handbook, tool-start pages, moderator console, onboarding flow, and community rules. Use examples that match real tasks. A one-page summary can link to detailed procedures, but it should still state the prohibited data categories, review triggers, reporting route, and consequences for misuse.
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Train for limitations, not just features
Training should cover approved tools, privacy and security, hallucinated facts, biased or incomplete outputs, prompt-injection risks, copyright and attribution questions, accessibility, and escalation. Practice with realistic examples: a résumé-screening suggestion, a support reply containing personal data, an automated account removal, and a generated image that requires disclosure.
Monitor and audit
Track which tools and use cases are approved, incidents and near misses, review completion, appeals, reversals, complaints, and measurable disparities where appropriate. Audit a sample of outputs rather than assuming that a tool remains safe because it passed an earlier review. NIST’s Govern Playbook recommends monitoring, auditing, change management, stakeholder engagement, and incident response, and says policies should include third-party AI systems.
Set a review cadence and change triggers
Name a routine review interval and also require an unscheduled review when a tool changes its model, retention terms, access controls, or intended purpose; when a new data category is introduced; when an incident occurs; or when law or authoritative guidance changes. Record the policy version, approver, effective date, and next review date.
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- Inventory uses. Ask teams, contractors, moderators, and administrators where they already use AI, including embedded features and personal accounts.
- Map data. Identify the information each use receives and apply the organization’s existing classification and retention rules.
- Assess impact. Note who could be harmed by an incorrect, biased, exposed, or unavailable output and whether the result can be reversed.
- Assign a tier. Place the use in an allowed, restricted, or prohibited category, recording the reason.
- Approve tools. Verify account ownership, security settings, retention, sharing, contractual terms, and administrative controls for restricted uses.
- Define review. Name the reviewer, checklist, evidence, timing, escalation path, and record required before a consequential output is acted on or published.
- Write audience-specific rules. Publish employee instructions separately from member-facing community rules, even when the underlying principles are shared.
- Train and launch. Use examples from actual workflows and provide a simple question and incident-reporting channel.
- Test the process. Run a tabletop exercise for accidental disclosure, a biased recommendation, an incorrect moderation action, and a vendor change.
- Measure and revise. Review incidents, appeals, overrides, and user feedback on the stated cadence and whenever a change trigger occurs.
Workplace and community controls at a glance
| Policy question | Workplace focus | Online-community focus |
|---|---|---|
| Who is covered? | Employees, contractors, managers, system owners, and reviewers | Members, moderators, administrators, and platform-governance staff |
| What is controlled? | Work tools, sensitive data, work product, records, and employment or access decisions | User-submitted content, AI disclosure, automated moderation, ranking, enforcement, and appeals |
| Who is accountable? | Business owners, managers, privacy or security leads, and qualified decision reviewers | Moderators, community administrators, appeal reviewers, and platform owners |
| Most important safeguards | Data classification, approved tools, documented human verification, fairness review, and escalation | Transparent notices, consistent enforcement, accessible information, meaningful appeals, and stakeholder input |
Common failure modes and fixes
“We banned AI,” but nobody defines it
Users then rely on personal accounts or hidden features. Replace the slogan with a scope statement, approved-tool list, and risk tiers. If a ban is necessary for a particular data class or decision, name that boundary precisely.
“A human reviewed it” without a real review
Rubber-stamping leaves the organization exposed to the same error as full automation. Require a checklist, source verification, authority to reject, and a record for consequential uses.
The policy protects data but ignores people affected by decisions
Confidentiality is only one risk. Add discrimination testing, accessibility checks, explanation and appeal rights, and a process for correcting an erroneous outcome.
Community members learn about automation only after enforcement
Publish a plain-language notice before use, identify which actions can be automated, and explain how a person can appeal. Update the notice when the system’s role changes.
The policy is never revisited
Assign an owner, date routine reviews, and tie reviews to tool, data, incident, and legal changes. Archive old versions so staff can determine which rule applied to an earlier decision.
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