The short version
Key takeaways
- Make approved and prohibited uses concrete.
- Match review to consequence and data sensitivity.
- Update policy when tools and workflows change.
Define the AI use policy outcome
An AI policy should help a person decide what they may do at the moment they choose a tool or submit information. Broad statements about using AI responsibly do not resolve whether customer records, contracts, source code, personnel information, or unpublished strategy may enter a service.
Inventory tools already in use, the jobs employees use them for, information entered, outputs distributed, and accounts paying for access. Record current review practices and any contractual, privacy, professional, or industry requirements that affect those workflows.
Approve a use only when the data boundary, accountable reviewer, permitted output, and incident path are clear enough for the person doing the work.
Build the AI use policy decision model
Use four review areas to make the choice visible. Give each area an owner, evidence, and an explicit threshold rather than relying on a general impression.
| Review area | Question and evidence |
|---|---|
| Allowed work | Name specific jobs, approved accounts, and output destinations. |
| Data boundary | Classify information that is allowed, restricted, or prohibited. |
| Human review | Match review depth to the consequence of a wrong output. |
| Accountability | Name the tool owner, business owner, and incident contact. |
Put the workflow into practice
Write the first version around decisions employees actually face, then test it with realistic scenarios. Keep the main policy short and link to role-specific examples, approved-tool details, and security procedures that can change without rewriting every principle.
- List current AI tools and owners, including free personal accounts.
- Classify common data examples using language employees recognize.
- Define approved, review-required, and prohibited uses by role.
- Publish how to report a bad output, exposure, or unexpected tool behavior.
- Train with scenarios and collect questions for the next revision.
Connected decisions worth reviewing next: AI Tools for Small Business: How to Choose a Useful, Safe First Use Case; How to Build and Govern an AI Business Knowledge Base; Small Business Cybersecurity Checklist: Priorities That Protect Operations.
Handle exceptions and failure paths
A sales coordinator may use an approved tool to summarize a public webinar, but may not paste a prospect contract or private pricing exception into a personal AI account. The policy directs contract analysis to an approved controlled workflow and requires a named reviewer before any customer-facing use.
Common mistakes to prevent
- Copying a generic policy that does not match actual tools.
- Banning all use while ignoring unsanctioned use already occurring.
- Treating human review as a phrase without naming the reviewer.
- Forgetting contractors, browser extensions, integrations, and mobile apps.
A policy is not a substitute for technical controls. Restrict access, configure approved services, monitor material changes, and remove tools that cannot meet the stated rules.
Measure and improve AI use policy
Choose a small set of signals that show quality, flow, risk, and outcome. Record the baseline before changing the process so improvement can be distinguished from activity.
| Signal | How to use it |
|---|---|
| Policy acknowledgments | Confirms who received the current rules. |
| Unapproved-tool findings | Reveals where the approved set does not meet real needs. |
| Reported incidents | Shows where guidance or controls failed. |
| Review exceptions | Tracks higher-risk uses and accountable approval. |
| Revision age | Prevents a static policy from outliving the tool environment. |
Review after a material vendor, model, integration, legal, or data-practice change and on a scheduled cadence. Use employee questions and incident reviews to replace vague prohibitions with clearer decisions and safer alternatives.
Common questions
Frequently asked questions
Does an AI policy need to name every tool?
Maintain an approved-tool register with owners and settings, while the core policy defines principles and decision rules that survive normal product changes.
Should every AI-generated output be disclosed?
Disclosure depends on audience, context, applicable obligations, materiality, and risk. Define role-specific requirements and obtain qualified advice where needed.
References and examples
Primary sources and product examples used to ground this guide. Product links are editorial references, not endorsements.