AI Policy Generator
Choose which AI uses are allowed, what needs approval, and what people must disclose. Then review and edit your policy.
Free to use, with no account needed. Only Word and PDF downloads require an email.
Build your policy
- 1Your organizationOrganization
- 2AI useAI use
- 3Rules and responsibilitiesRules
- 4ReviewReview
Why an AI policy matters
Back to sectionsAI use creates decisions before it creates documents: what information someone may share, which parts of a task they may delegate, and who is accountable for the result. Without a shared policy, people have to guess—or receive different answers from different managers, instructors, and editors.
Make useful AI use easier to recognize
A policy is not just a ban list. Clear permission for low-risk activities lets people distinguish legitimate assistance from uses that need approval. They should know whether brainstorming, translation, coding, or drafting is allowed before they start—not after they submit the work.
Protect information before it leaves your control
Student records, client material, unpublished research, and internal documents need boundaries before anyone enters them into a service. Specify the information, accounts, and features that are approved. A familiar product name is not enough to establish suitable data handling.
Keep a person accountable for quality and rights
A convincing answer can still contain invented sources, incorrect calculations, insecure code, or material the organization has no right to use. Name who checks the output and who approves consequential decisions. Producing a draft is not the same as verifying it.
Set fair expectations for authorship and disclosure
People need to know what counts as their own work and when assistance must be disclosed. Explain the rules in advance, including exceptions and a fair review process. An AI-detector score should not be the sole basis for an accusation or disciplinary decision.
Give questions and incidents a clear route
When a tool changes or something goes wrong, staff and students should not have to work out who is responsible. Identify an owner, an approval route, and a reporting contact so uncertain uses can be reviewed before they become routine.
Different settings, different stakes
A written policy can make expectations and accountability clearer. It does not, by itself, prevent misuse, prove compliance, or replace training and human oversight.
How AI use and policy expectations are changing
Back to sectionsWorkplace AI use has grown substantially since 2023. Governance guidance has also become more specific, while institutions continue to develop their own rules. These are related developments, but usage rates and policy-adoption rates measure different things.
Reported AI use at work
Share of U.S. employees reporting AI use in their role, including occasional use—not just frequent or daily use.
Selected observations from Gallup’s U.S. workforce surveys, not a continuous annual series or a forecast. The 2025 report describes use at least a few times a year. The Q2 2026 survey was fielded May 6–20 among 22,573 employed U.S. adults. These figures do not measure student use or the percentage of organizations with an AI policy.
Using AI is not the same as having clear rules
In its 2025 report, Gallup found that 30% of employees said their organization had general guidelines or formal AI-use policies. That is employee-reported awareness of guidance, not an audit of whether organizations had a policy or followed it. The practical lesson is to make approved rules easy to find and understand.
What to plan for next
These are planning priorities suggested by the evidence, not predictions or universal legal requirements.
- Rules for tasks, not only tool namesReassess permissions when an assistant gains access to files, connected services, or actions it can take on someone’s behalf. Permission to draft text should not automatically authorize sending messages, changing records, or making decisions.
- Implementation alongside the documentUse examples, training, and accessible alternatives to explain the boundaries. Decide how people ask questions and how concerns are reviewed, rather than relying on an acknowledgment or automated detection alone.
- A policy that can be revisedAssign a review owner and record versions. Review after material changes to tools, data access, incidents, contracts, or applicable requirements, as well as on a realistic schedule. Communicate what changed and when it takes effect.
Dated developments in AI governance
A shared framework for managing AI risk
NIST released its AI Risk Management Framework as voluntary guidance. It gives organizations a basis for considering trustworthiness and risk throughout AI design, use, and evaluation—not a certificate that a generated policy can confer.
Guidance becomes specific to generative AI
NIST added a Generative AI Profile as a companion to the framework. For policy owners, this is a reason to translate broad principles into rules for the actual activities and systems people use.
Education institutions are developing formal guidance
In UNESCO’s survey, 19% of respondents said their institution already had a formal AI policy, while 42% reported guidance under development. The survey received 400 responses from UNESCO Chairs and UNITWIN Networks across 90 countries, with a 38% response rate. It describes that responding network, not a representative rate for all schools or universities.
What does the generator include?
Back to sectionsThe questionnaire turns your AI-use decisions into a draft for your organization to review and approve.
- Built for
- Companies, law firms and in-house legal teams, schools, universities, individual teachers, and publishers.
- Cost
- Free to create, edit, copy, share, and download. No account required.
- Your rules
- Four permission levels: allowed, conditional, approval required, and prohibited.
- Your documents
- A policy, activity matrix, summary, and implementation checklist.
- File downloads
- Word (DOCX) and PDF. Email required for downloads only.
- Sharing
- An author-confirmed public snapshot link. No email required; expires after 30 days unless revoked earlier.
AI policy templates by audience
Back to sectionsWorkplace AI policy
Specify which tools employees can use, what data they can enter, and who reviews their work.
University AI policy
Set university-wide rules and define when instructors can make exceptions for their courses.
School AI policy
Define how students and staff may use AI, which tools are approved, and what families need to know.
Syllabus AI policy
Tell students which uses of AI are allowed in your course and how to disclose them.
Law & Legal AI policy
Set matter and vendor boundaries for client information, research, supervision, communications, fees, and reporting.
Publishing AI policy
Set rules for AI-assisted writing, source checks, copyright, and editorial approval.
Policy resources
What is an AI policy generator?
Back to sectionsThe generator turns your decisions into a structured policy. You choose which activities are permitted, what data is off limits, when people must disclose AI use, and who approves exceptions.
The rules cover employees, lawyers and legal teams, educators, students, researchers, writers, and editors. Workplace policies, legal-team policies, school-wide policies, and course statements need different boundaries, so the questions and draft change with the audience.
What should an AI usage policy include?
“Use AI responsibly” is not enough to answer everyday questions. Can an employee summarize a customer conversation? Can a student submit an AI-written paragraph? Can an editor use AI to check grammar without disclosing it? A useful AI acceptable use policy distinguishes the activity, the information involved, and the review required.
Approving a product such as ChatGPT, Claude, Gemini, or Microsoft Copilot does not approve every account type, feature, data category, or use.
- Purpose and scope: identify the employees, contractors, students, instructors, departments, courses, or publications covered by the policy.
- Approved AI tools: explain which tools people may use, how new tools are approved, and where to ask about unfamiliar features.
- Activity permissions: distinguish allowed use, use with specific conditions, use requiring approval, and prohibited use. Research, drafting, coding, and final submission do not need identical rules.
- Data protection: set boundaries for personal information, confidential material, student records, customer data, unpublished work, and credentials.
- Human review: require the responsible person to check factual claims, citations, calculations, rights, bias, safety, and suitability before relying on an output.
- Disclosure and attribution: say when people must identify AI assistance and what a truthful disclosure should contain.
- Ownership and reporting: name the policy owner, approver, reviewer, and contact for questions, incidents, or suspected misuse.
- Training, exceptions, and review: explain how rules are communicated, how exceptions are authorized, and when the policy is revisited.
Create your policy in four stages
You can begin before every decision is final; unresolved items remain visible for review. An organization name is optional, and you can copy a draft without an account.
- Choose your audience and scope. Start with a workplace, law firm or in-house legal team, school, university, individual teacher, or publisher. A matter-specific legal rule or course-specific AI policy should not silently replace professional, institutional, or client requirements.
- Set your AI-use rules. Choose a restricted, permission-based, or broadly allowed starting approach, then adjust the permission, conditions, and clarification for each activity.
- Add rules and responsibilities. Select permitted data categories, disclosure requirements, approved tools, accountable roles, training expectations, and a review schedule.
- Review the complete policy. Check the activity matrix, resolve open decisions, edit sections, and copy the result or prepare a Word or PDF download.
Set permissions by activity
The same tool can be suitable for one task and unsuitable for another. These examples show possible rules, not universal defaults or approved legal wording.
- Brainstorming from public information: allowed, with a person checking the ideas before using them.
- Summarizing an internal draft: allowed with conditions, such as an approved tool, appropriate data permissions, and human verification.
- Processing student, employee, or customer records: requires a separate review of the purpose, data, tool, contracts, and applicable requirements; routine tool approval is not enough.
- Entering passwords or access tokens into a general-purpose AI assistant: prohibited.
- Publishing or submitting AI-assisted work: subject to the relevant authorship, disclosure, factual-review, and assessment rules.
Write your policy
Edit, copy, or download your AI policy
The result includes an editable policy, activity-permission matrix, summary, and implementation checklist. Course policies can include a syllabus statement, and acknowledgment text is available where relevant. Read the full policy because the summary omits some conditions.
Creating, reading, editing, and copying are free and do not require an account or email address. To download a Word document (DOCX) or PDF, submit the download form with an email address. Marketing consent is optional and is not selected by default. Files are generated in your browser after the form is saved; the tool does not email the policy to you.
Ordinary drafts and downloaded files remain in your browser. If you explicitly confirm sharing, the tool temporarily stores only the selected public document as an immutable snapshot, including any names or contact details present in that document. Anyone with the link can view and forward it. The link expires after 30 days, or you can revoke it earlier. Clearing browser storage can remove your management access to revoke the snapshot. Revocation cannot recall copies other people already made.
Review and adopt the draft
Before adoption, test the draft against real situations. Ask someone covered by it to find out whether they may use AI for a first draft, what information they may enter, or who can approve an exception. If the answer is unclear, revise the rule.
Assign an owner, communicate the policy, and make the current version easy to find. Review it when tools, use cases, incidents, contracts, or institutional requirements change, as well as on your chosen schedule. A detector result or automated indicator should not be the sole basis for concluding that someone broke the policy; use human review and the applicable process.
This tool provides a draft, not legal advice or an expert-approved policy. Your obligations depend on your location, sector, contracts, data, and uses of AI. Entering a region does not produce a jurisdiction-specific legal assessment, and using the generator does not establish regulatory compliance or ISO certification.
- Resolve undecided permissions and assign each responsibility.
- Have the appropriate privacy, security, academic, employment, and legal owners review the draft.
- Explain the rules with examples and tell people where to ask questions.
- Record approval, effective date, review date, and any authorized exceptions.
AI policy settings explained
Back to sectionsOrganization and scope
- Audience
The audience determines which activities and responsibilities appear in the policy. Choose the people the policy governs, not the person writing it.
- University: institution-wide academic and research use. School: K–12 students and staff. Teacher: one class or syllabus.
- Company: employees and workplace use. Publisher: writers, editors, and contributors.
- Organization name
Enter the institution, employer, school, or publishing team adopting the policy. The generator inserts this name in the draft. If you leave it blank, the draft uses a generic description.
Example: For a school-wide policy, enter the school’s name. Use Policy Scope to limit the policy to a department.
- Policy scope
Scope sets where the policy applies: an entire organization, a department, one course, or a publication. A narrower policy remains subject to any controlling organization-wide rules.
Example: A course AI policy may restrict assessed work without replacing university-wide privacy or research requirements.
- Operating region
Enter the places where the policy will apply so reviewers can check local obligations. This field provides context only; it does not tailor the draft to a jurisdiction, give legal advice, or establish compliance.
Example: A team operating in several countries can list those countries for its legal or privacy reviewer.
- Course name
Name the class covered by a course-level policy or syllabus statement so students can distinguish it from other course rules.
Example: English 101 — Academic Writing, Fall term.
- Education level
Select the learners’ level. When reviewing the draft, account for their age, learning objectives, supervision, and access.
Example: For younger students, review tool age requirements and school privacy arrangements separately; this selection does not check them.
- Industry
Enter the organization’s sector as context. This field does not change the policy clauses. Add any sector-specific rules and arrange a separate compliance review where needed.
Example: A healthcare organization should separately review patient information restrictions and the suitability of each AI tool.
- Using or building AI
Using third-party tools means working with an existing service. Building AI systems includes developing or integrating models and AI-enabled products. Choose Both if both apply.
- Tool use requires approved accounts, data limits, and human review.
- System development may also require testing, monitoring, vendor review, and further governance.
- Organization size
Select the approximate size of the organization or group covered by the policy. This field provides context only; it does not change the clauses or assign responsibilities.
Example: A small team may assign several responsibilities to one role; a larger institution may need separate owners and reviewers.
Use, permissions, and approved tools
- Default rules
The default rules set initial activity rules. They do not decide every use. Review each rule in the permission matrix.
- Restricted: AI use is prohibited unless a documented exception applies.
- Permission Based: AI is allowed for specified activities under stated conditions.
- Broadly Allowed: approved AI use is encouraged within defined limits; use is not unrestricted.
- Activity permissions
The permission matrix sets a rule for each activity. Permission for an activity does not approve a particular tool or data type.
- Allowed: permitted under the policy’s general safeguards.
- Allowed with conditions: permitted only when every listed condition is met.
- Approval required: obtain authorization before proceeding.
- Prohibited: do not use AI for this activity.
Example: Brainstorming may be allowed while publishing an AI-assisted statement requires review and approval.
- Conditions
Conditions are requirements attached to an activity, such as using an approved tool, excluding personal information, or checking factual claims. Write each requirement so users and reviewers can verify it.
Example: For AI-assisted research: verify citations against the original sources before using the output.
- Clarifications and exceptions
Use this field to define an activity rule’s boundary or explain a documented exception. State the exception’s limits and who may approve it. Do not use it to contradict the selected permission.
Example: A manager may approve a limited pilot using public sample data; customer records remain excluded.
- Approved tools
List the services, account types, or managed environments the organization has approved. A product name alone does not approve every feature, account, task, or data category.
- Separate tool names with commas.
- Leaving the list blank keeps tool approval case-by-case; it does not authorize every AI service.
Example: Specify an organization-managed account rather than assuming a personal account has the same protections.
- Assignment-specific rules
This setting controls whether instructors may set assignment-specific rules. “Within institutional authority” means they may change defaults only where the institution allows it.
- No overrides allowed: assignment rules cannot replace these defaults.
- Within institutional authority: instructors can specify task rules without overriding higher-level privacy, security, or academic requirements.
Example: An instructor may prohibit AI drafting for an essay even if brainstorming is generally allowed.
Data, disclosure, training, and responsibilities
- Data handling
Data handling rules specify what users may enter into approved tools. Selecting a category does not replace privacy review, contractual restrictions, or permission to use particular records.
- Public Information: material legitimately available to the public. Internal Drafts: non-public work in progress.
- Confidential Data: business or institutional information requiring restricted access. Personal Information: information that identifies or relates to a person.
- Student Records: learner information and education records. Customer Data: information obtained from or about customers.
- Do not enter credentials, secrets, or protected health data into general-purpose AI tools.
- Disclosure
Disclosure tells readers, instructors, clients, or reviewers when AI contributed to the work. It does not replace verification or make a prohibited use acceptable.
- Always required: disclose any AI assistance.
- Only for substantive generation: disclose meaningful AI-created text, ideas, analysis, code, or other content, rather than routine minor assistance.
- For external or assessed work: disclose AI assistance in material shared outside the organization or submitted for assessment.
- According to specific assignment rules: follow an identified task or assignment requirement. State that rule clearly.
Example: “AI was used to draft an outline. I checked the sources and wrote the final text.”
- Custom disclosure rule
State what the disclosure must include, where it belongs, and when it is required. Explain the selected disclosure setting without contradicting it.
- If you select “According to specific assignment rules,” complete this field so readers know which rule to follow.
Example: Add a note at the end identifying the AI tool, what it helped with, and who reviewed the result. If prompts are required, exclude confidential or personal information.
- Training and policy review
Training teaches people how to follow the policy. Policy review checks whether the rules still fit the organization’s tools, activities, and risks. Set each separately.
Example: Train new staff before AI use, provide a yearly refresher, and review the policy every six months.
- Onboarding
Require people to learn the rules when they join or before they use approved tools. Cover permissions, restricted data, disclosure, human review, and where to ask questions.
Example: A new employee reviews the policy and practices deciding which information can be included in an AI prompt.
- Annual refresher
An annual refresher reviews the rules and explains important changes to existing users. It does not set the schedule for reviewing the policy itself.
Example: Use a yearly session to discuss new tools, common mistakes, and updated disclosure expectations.
- Review frequency
Choose whether the policy owner should formally reassess the policy quarterly, every six months, or annually.
- Review sooner after an incident, a significant tool change, or a material change in obligations.
- Selecting a frequency records an expectation in the draft; the generator does not schedule meetings or send reminders.
- Ownership and reporting
Assign separate responsibilities for maintaining the policy, approving tools and exceptions, reviewing outputs, and handling reports. The reporting contact is the channel people use; the reporting role handles the reports.
Example: An owner maintains the policy, an approval role authorizes exceptions, a reviewer checks AI-assisted work, and a designated team handles reports.
- Policy owner
The owner maintains the policy, coordinates implementation, and oversees policy reviews. Enter a role or title rather than a person’s name so responsibility remains clear when staff change.
Example: Head of IT, Principal, Research Governance Lead, or Managing Editor.
- Approval role
The approver authorizes tools, proposed uses, and documented exceptions. This role is distinct from the policy owner and the person who reviews outputs. You must enter an approval role before labeling the draft Approved.
Example: An authorized manager or institutional lead reviews a proposed AI use before granting an exception.
- Output reviewer
The reviewer checks AI-assisted outputs before they are relied on, submitted, or released. Choose a role that can assess accuracy, sources, suitability, and relevant risks. The policy owner reviews the policy itself.
Example: A subject-matter expert verifies an AI-assisted report’s claims and citations before publication.
- Incident reporting role
Name the role or team that receives and coordinates responses to suspected misuse, unsafe outputs, or other policy concerns. Enter a responsibility here, not an email address.
Example: A compliance officer receives reports and involves the security or academic team when needed.
- Reporting contact
Provide a monitored channel for questions and reports of suspected misuse. Enter an address, internal help route, or other contact method the organization maintains.
Example: An internal service-desk queue monitored by the reporting team. Adding a contact does not send reports or create a mailbox.
- Policy status
Draft means the document is still being prepared or reviewed. Select Approved only after your organization’s authorized approval process is complete.
- Changing this label records a status; it does not obtain approval, certify compliance, or make the policy legally effective.
- Advanced governance options
These options add responsibilities, acknowledgment wording, audience-specific safeguards, and document-control details. Select only measures the organization will implement.
- Including a clause records an expectation. The generator does not enforce it, collect signatures, or verify compliance.
- AI system governance
This workplace module adds requirements for leadership, objectives, vendor review, and AI governance.
- The module refers to voluntary NIST AI Risk Management Framework guidance. Including it is not an audit, certification, or proof of compliance with a standard.
- Acknowledgment
This option adds a form for users to confirm that they received and understood the policy.
- It does not prove compliance or replace training. The generator includes the form; your organization collects and retains acknowledgments separately.
- Communication with families
This school option requires communication with families about how students may use AI, the safeguards that apply, and where to ask questions.
- Family communication is not automatically consent for collecting or processing a child’s information. Review any consent requirements separately.
- Equitable access and alternatives
This school option requires suitable alternatives when students cannot use an AI tool. Consider cost, accessibility, devices, accounts, and learning needs.
Example: Provide an equivalent non-AI activity when a student cannot access an assigned tool. The clause does not itself provide devices, subscriptions, or accommodations.
- Document version
A version label helps readers and reviewers identify which edition of the policy applies. Update it when you issue a revised document.
Example: Use 1.0 for an initial release and 1.1 for a later revision. This field is a document label, not automatic version history.
- Effective date
The proposed effective date records when the rules are intended to start applying. Confirm it before adoption, allowing time for approval and communication.
- Entering a date does not approve or automatically activate the policy.
- Next policy review date
Set the next planned review date, consistent with the review frequency. This is when the responsible people should reassess the document, not necessarily when it expires.
- Review earlier if circumstances change. This date does not create a calendar event or send a reminder.
Review and use
- Review the policy draft
Before adopting the policy, check unresolved decisions, activity permissions, responsibilities, and any text you edited. The generated policy is a draft, not legal advice or external approval.
- Use Save changes to keep an edited section before switching policies.
- Reading, editing, copying, and sharing do not require email. A Word or PDF download requires the download form; marketing consent remains optional.
- Sharing publishes an immutable snapshot of the selected document for anyone with the link. The link expires after 30 days and can be revoked earlier.
AI policy examples to adapt
Back to sectionsThese are excerpts, not complete policies. Edit them to match your rules.
Workplace clause
Classroom clause
Publishing clause
AI policy generator FAQs
Back to sectionsDo we need an AI policy before officially adopting AI?
Setting boundaries before adoption helps people understand whether they may use personal accounts, built-in AI features, or AI services offered by vendors. If your organization has not approved AI use, say what is restricted, who can review a proposed use, and what alternatives are available. Not approving a tool does not ensure that nobody encounters or uses it.
Is the AI policy generator free?
Yes. You can create, read, edit, and copy the full draft without providing an email. The download flow asks for an email and optional lead details before preparing document files. You do not need to create an account.
Can I create an AI policy for employees or my company?
Yes. Choose Company or Nonprofit to build a workplace AI policy covering approved tools, employee activities, confidential information, human review, disclosure, and accountable roles. Adapt the draft to your business and have the responsible owners review it.
Can teachers create a classroom or syllabus AI policy?
Yes. Choose Individual Teacher for course-level permissions and a syllabus statement, or School or District for institution-wide rules. Describe the permitted assistance, disclosure requirements, and assignment-specific authority so students know what is expected.
Can I use this as a ChatGPT policy generator?
Yes. You can create rules for using ChatGPT and other generative AI tools, including Claude, Gemini, and Microsoft Copilot. Name the tools you approve and specify the activities and data allowed; approval should not be inferred from a product name alone.
Can I edit and download the policy in Word or PDF?
Yes. Edit the policy on screen and copy the text without an email. For DOCX or PDF downloads, complete the email form first. The files download from your browser, and marketing consent is optional.
Can I share a policy without providing an email?
Yes. Sharing is ungated and requires your confirmation before the selected document is stored online. The public snapshot is fixed, expires after 30 days, and can be revoked earlier using the management access kept in this browser. Anyone with the link can view or forward it, and revocation cannot recall copies. Remove names, contact details, or other information you do not want public before sharing.
Does the draft prove legal compliance or ISO certification?
No. It is not legal advice, an audit, a conformity assessment, or evidence of ISO/IEC 42001 certification. Applicable duties depend on your jurisdiction, role, data, contracts, and uses.
How often should an AI policy be reviewed?
Choose a cadence your owner can actually maintain, and also trigger review after material changes to tools, uses, law, incidents, contracts, or institutional rules. Record reviews when they happen rather than assuming a future review has occurred.
Does Pangram or an AI model write my policy on the server?
No. The policy is assembled in your browser from your selections and stored text, not written by a server-side AI. Ordinary drafts are not sent to the backend or an AI provider. Only when you explicitly confirm sharing is the selected public document sent to temporary snapshot storage.
Are my policy answers saved?
Yes. Answers, saved text edits, and your place in the questionnaire autosave in this browser. Use My policies to create, name, reopen, duplicate, and edit separate drafts. Drafts remain available after closing a tab, but do not sync across browsers or devices, and clearing site data removes them. Copy or download important policies for a separate record. Confirmed public snapshots and download-form contact details use separate storage and notices; neither turns your browser drafts into a synced account.
Sources and references
These drafts have not been reviewed by a lawyer or subject-matter expert. Have your legal or policy team review them before use. They are not legal advice or proof of compliance.
NIST describes its AI Risk Management Framework as voluntary guidance for managing AI-related risks and incorporating trustworthiness into AI use.
Use it as a review reference when assigning owners and considering risks, safeguards, and ongoing oversight. This generator does not perform a NIST assessment.
UNESCO’s guidance emphasizes a human-centred approach, data privacy, and age-appropriate use of generative AI in education and research.
For school, university, and classroom policies, review student data, age restrictions, access, and the learning purpose of each permitted activity with the appropriate institutional owners.
Pangram’s classroom-policy article compares blanket restrictions with a tier-based approach that allows different levels of AI assistance for different assignments.
Use the examples to discuss classroom boundaries, then choose your own permissions in the generator. These approaches are options, not a requirement to adopt a particular rule.
References do not imply endorsement.