AI Governance Policy Template
An AI governance policy template sets out how your organization decides, oversees, and documents its use of AI: who approves new AI uses, how risk is assessed, what gets recorded, and how systems are monitored once they are live. Use this free AI governance policy template as the organization-level framework that sits above your staff-facing rules for everyday AI use.
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ChatGPTI need to write an AI Governance Policy. Act as an expert technical writer and draft one for me. Use this structure, keeping each section clear, concise, and genuinely useful to the reader: 1. Policy details 2. Purpose 3. Scope 4. Principles 5. Roles and responsibilities 6. AI inventory 7. Risk classification 8. Approval lifecycle 9. Third-party and vendor AI 10. Monitoring 11. Incidents 12. Records and documentation 13. Training 14. Review Before you begin, ask me for the details you need: the product, team, or audience this is for, and anything specific I should include. Then write the full AI Governance Policy in clean, well-formatted Markdown.
Write an AI Governance Policy using the structure below. Keep each section clear, concise, and genuinely useful to the reader. Structure: 1. Policy details 2. Purpose 3. Scope 4. Principles 5. Roles and responsibilities 6. AI inventory 7. Risk classification 8. Approval lifecycle 9. Third-party and vendor AI 10. Monitoring 11. Incidents 12. Records and documentation 13. Training 14. Review Use rich formatting so it reads well in a knowledge base: - Clear headings and subheadings for each section. - Callout boxes for asides: a Tip callout for advice, a Note callout for helpful context, and a Warning callout for anything a reader must not miss. - Tables for structured or comparison content. - Numbered lists for sequential steps and bulleted lists for options. - Bold for key terms and any values people must not get wrong. Where the content genuinely calls for it, also use these interactive blocks (otherwise keep to the formatting above): - A Mermaid diagram for a flow, process, or set of relationships that reads more clearly as a picture than as text.
Connected HelpDocs over MCP? Add "save this as an unpublished HelpDocs article" to the prompt and the draft lands in your knowledge base, ready to review.
## The AI governance policy template ### 1. Policy details Include the policy name, its version, the effective date, who owns it, who approved it, and when the next review is scheduled. ### 2. Purpose This policy is here to keep your AI use effective, lawful, and consistent with your values. It also puts clear accountability behind every system, so you always know who owns what. ### 3. Scope Spell out what this policy reaches: AI systems you develop, AI tools you purchase, AI features built into other software, and AI agents that operate within your systems. Call out exactly which teams, regions, and subsidiaries are in scope. ### 4. Principles Every AI use has to meet a short set of commitments, spelled out in plain language. A few of them: - **Accountability:** each AI system comes with a named person responsible for it - **Human oversight:** people step in to review any decision with real consequences for others - **Transparency:** people are told when AI is involved, at least where it matters - **Privacy and security:** data in AI systems gets the same protection as everywhere else - **Fairness:** systems get checked for unfair outcomes before and after going live ### 5. Roles and responsibilities Here you'll see who owns each task. Name roles, not people, when you write these in. - **Executive sponsor:** owns the policy overall and settles escalations that need a final call - **Governance group:** looks over higher-risk uses and refreshes the policy as needed - **System owner:** takes responsibility for a single AI system from start to finish - **Legal, security, and privacy:** each check uses that touch their domain - **All staff:** stick to the [AI policy](/templates/ai-policy/) and flag anything that worries you ### 6. AI inventory Before any AI use goes live, it gets logged in one central register. List the fields each entry needs to carry. | Field | What to record | | --- | --- | | System name | TODO | | Owner | TODO | | Purpose | TODO | | Data used | TODO | | Risk tier | TODO | | Vendor | TODO | | Approval date | TODO | | Next review | TODO | ### 7. Risk classification Here's how AI uses get bucketed into risk tiers, and what each level asks of you. Weigh what the system touches (people, money, safety, legal obligations) against how closely you check its output. Anything in a higher tier calls for a full [AI risk assessment](/templates/ai-risk-assessment/) plus sign-off from the governance group. | Tier | Typical examples | Requirements | | --- | --- | --- | | Low | TODO | TODO | | Medium | TODO | TODO | | High | TODO | TODO | ### 8. Approval lifecycle Walk through the journey every new AI use takes: proposal, risk classification, assessment, approval, launch, and retirement. Then name the approver for each tier and the evidence they want in hand before signing off. ### 9. Third-party and vendor AI Before you buy a vendor's AI tool, check how it handles data, how it keeps things secure, where processing happens, whether it trains on your data, and what the contract terms cover. You'll also want to catch and review AI features that get added to tools you already use. ### 10. Monitoring Keep tabs on the systems running in production, watching for performance issues, accuracy problems, drift, complaints, and shifts in how people use them. Choose how frequently each tier gets reviewed. ### 11. Incidents Define what you count as an AI incident, describe how it gets reported, and name who does the investigating. Then show how the lessons get folded back into your inventory and the policy. Include a link to your [incident report template](/templates/incident-report/). ### 12. Records and documentation Know what documentation each system needs and how long you'll keep it. That covers assessments, approvals, testing results, and changes. Keeping those records in good shape is what lets you respond when customers, auditors, or regulators come asking questions. ### 13. Training The training system owners, reviewers, and staff need, plus how frequently it gets updated. ### 14. Review How often you revisit the policy, and the circumstances that call for a review ahead of schedule, such as adopting a new kind of AI use or a shift in legal obligations.
The AI governance policy template
Take this structure and paste it into a new article, then swap out the guidance under each heading for your organization’s own details. Treat the template as a starting point rather than legal advice, and have your legal and compliance advisors review the finished policy before you adopt it.
1. Policy details
Include the policy name, its version, the effective date, who owns it, who approved it, and when the next review is scheduled.
2. Purpose
This policy is here to keep your AI use effective, lawful, and consistent with your values. It also puts clear accountability behind every system, so you always know who owns what.
3. Scope
Spell out what this policy reaches: AI systems you develop, AI tools you purchase, AI features built into other software, and AI agents that operate within your systems. Call out exactly which teams, regions, and subsidiaries are in scope.
4. Principles
Every AI use has to meet a short set of commitments, spelled out in plain language. A few of them:
- Accountability: each AI system comes with a named person responsible for it
- Human oversight: people step in to review any decision with real consequences for others
- Transparency: people are told when AI is involved, at least where it matters
- Privacy and security: data in AI systems gets the same protection as everywhere else
- Fairness: systems get checked for unfair outcomes before and after going live
5. Roles and responsibilities
Here you’ll see who owns each task. Name roles, not people, when you write these in.
- Executive sponsor: owns the policy overall and settles escalations that need a final call
- Governance group: looks over higher-risk uses and refreshes the policy as needed
- System owner: takes responsibility for a single AI system from start to finish
- Legal, security, and privacy: each check uses that touch their domain
- All staff: stick to the AI policy and flag anything that worries you
6. AI inventory
Before any AI use goes live, it gets logged in one central register. List the fields each entry needs to carry.
| Field | What to record |
|---|---|
| System name | TODO |
| Owner | TODO |
| Purpose | TODO |
| Data used | TODO |
| Risk tier | TODO |
| Vendor | TODO |
| Approval date | TODO |
| Next review | TODO |
7. Risk classification
Here’s how AI uses get bucketed into risk tiers, and what each level asks of you. Weigh what the system touches (people, money, safety, legal obligations) against how closely you check its output. Anything in a higher tier calls for a full AI risk assessment plus sign-off from the governance group.
| Tier | Typical examples | Requirements |
|---|---|---|
| Low | TODO | TODO |
| Medium | TODO | TODO |
| High | TODO | TODO |
8. Approval lifecycle
Walk through the journey every new AI use takes: proposal, risk classification, assessment, approval, launch, and retirement. Then name the approver for each tier and the evidence they want in hand before signing off.
9. Third-party and vendor AI
Before you buy a vendor’s AI tool, check how it handles data, how it keeps things secure, where processing happens, whether it trains on your data, and what the contract terms cover. You’ll also want to catch and review AI features that get added to tools you already use.
10. Monitoring
Keep tabs on the systems running in production, watching for performance issues, accuracy problems, drift, complaints, and shifts in how people use them. Choose how frequently each tier gets reviewed.
11. Incidents
Define what you count as an AI incident, describe how it gets reported, and name who does the investigating. Then show how the lessons get folded back into your inventory and the policy. Include a link to your incident report template.
12. Records and documentation
Know what documentation each system needs and how long you’ll keep it. That covers assessments, approvals, testing results, and changes. Keeping those records in good shape is what lets you respond when customers, auditors, or regulators come asking questions.
13. Training
The training system owners, reviewers, and staff need, plus how frequently it gets updated.
14. Review
How often you revisit the policy, and the circumstances that call for a review ahead of schedule, such as adopting a new kind of AI use or a shift in legal obligations.
Tip
AI governance falls apart when nobody can locate the latest rules. Start a free HelpDocs trial and publish your AI policies internally with built-in permissions and version history.
How to fill in this AI governance policy template
A governance policy should get sound AI decisions made faster, not drag them out. These habits will help:
- Start with a list. You can’t set rules for tools nobody has written down. Find out what AI your teams already run before drafting anything.
- Match the process to the risk. Low-risk uses can get a quick check. Reserve full reviews for systems that touch people, money, or safety.
- Point to one owner, not a group. Each system needs a single person accountable for it. Committees give input; the owner decides and stands behind the outcome.
- Leave staff rules out of this document. Everyday do’s and don’ts belong in your AI policy. Keep this one focused on oversight.
- Have it read by the right people. Legal, security, and privacy should review the draft and adjust it to fit your obligations.
- Revisit it regularly. AI moves fast, so pick a short review cycle and hold to it.
When something higher-risk needs approval, handle the assessment part with our AI risk assessment template.
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