Your team is using AI. The question is whether they’re using it safely.
MIT’s research on AI in business documented a “shadow AI economy” where only 40% of companies have official AI subscriptions, but 90% of workers use personal AI tools for job tasks. People bring their own ChatGPT accounts to work whether you sanction it or not.
This creates risk. Not theoretical risk. Real risk of data leaks, compliance violations, and quality problems.
An AI policy doesn’t stop people from using AI. It gives them guardrails so they use it safely. This guide provides a practical template you can adapt for your organization.
Why You Need a Policy Now
AI without policy is already happening. The choice isn’t whether to allow AI use. The choice is whether to guide it.
Data exposure risk. Employees paste customer information, financial data, and proprietary strategies into AI tools. Most consumer AI tools use this data for training. Your confidential information becomes part of the model.
Compliance risk. GDPR, CCPA, HIPAA, and industry regulations have data handling requirements. AI use can violate these requirements if not managed.
Quality risk. AI makes confident errors. Without review standards, those errors reach customers, partners, and public channels.
Consistency risk. Different people using AI differently produces inconsistent outputs. Brand voice, messaging standards, and quality expectations vary.
Legal risk. Questions about AI-generated content ownership, disclosure requirements, and liability remain unsettled. Policy provides defensible positions.
ISACA’s 2025 guidance emphasizes that with regulations like the EU AI Act taking effect, organizations can no longer treat AI governance as an afterthought.
Policy Framework Overview
A complete AI policy covers five areas:
- Approved Tools - What AI tools are sanctioned for use
- Data Rules - What information can and cannot be shared with AI
- Use Cases - What types of work AI can support
- Quality Standards - How AI outputs must be reviewed
- Disclosure Requirements - When AI use must be disclosed
Each section should be specific enough to guide decisions but flexible enough to accommodate evolving situations.
Section 1: Approved Tools
List which AI tools are approved for company use. Be explicit.
Template language:
Approved AI Tools
The following AI tools are approved for business use:
Enterprise-approved tools:
- [Tool name] - Approved for all business functions
- [Tool name] - Approved with restrictions (see Section 2)
- [Tool name] - Approved for [specific department] only
Prohibited tools:
- Consumer versions of AI tools (free ChatGPT, free Claude, etc.) for any business data
- AI tools without enterprise agreements
- AI tools that lack SOC 2 compliance
Requesting new tools: Requests to add AI tools require approval from [IT Security / Data Governance / designated role]. Submit requests through [process]. Evaluation criteria include:
- Data handling practices
- Security certifications
- Compliance with company requirements
Why this matters: Different AI tools have different data handling policies. Enterprise versions often provide better data protection than consumer versions. Being explicit prevents confusion.
Research from Liminal on AI governance notes that six core components form effective AI governance: policy development, risk assessment, compliance alignment, technical controls, ethical guidelines, and continuous monitoring.
Section 2: Data Classification Rules
The most critical section. What can and cannot be shared with AI.
Template language:
Data Classification for AI Use
NEVER share with AI:
Category: Confidential Data
- Customer personally identifiable information (PII)
- Employee personal information
- Financial records with identifying information
- Health information (PHI)
- Social security numbers, credit card numbers, account numbers
- Passwords, API keys, access credentials
- Proprietary source code
- Legal documents under privilege
- Board materials and non-public financial information
Category: Sensitive Business Data
- Unannounced product plans
- Merger and acquisition information
- Pricing strategies not yet public
- Partnership negotiations
- Information under NDA
May share with approved tools only:
Category: Internal Business Data
- Aggregated data without individual identification
- General business processes and procedures
- Marketing and sales materials (already public or intended for public)
- Internal communications (without confidential content)
- Draft content intended for external publication
May share freely:
Category: Public Information
- Published content
- Public marketing materials
- General industry information
- Publicly available data
When in doubt: If you’re unsure whether data can be shared with AI, assume it cannot. Ask [designated contact] for guidance before proceeding.
Implementation note: These classifications should align with your existing data classification scheme. If you don’t have one, this becomes an opportunity to create it.
Section 3: Acceptable Use Cases
Define what AI can be used for and what’s off-limits.
Template language:
Approved AI Use Cases
AI is appropriate for:
Content Creation
- Drafting emails, messages, and documents (with review)
- Generating initial content outlines
- Creating social media post variations
- Writing first drafts of marketing copy
- Summarizing documents and meetings
Research and Analysis
- Synthesizing publicly available information
- Analyzing trends and patterns
- Competitive research using public data
- Literature review and background research
Administrative Support
- Meeting agenda and summary creation
- Process documentation
- Template generation
- Formatting and editing assistance
Learning and Development
- Explaining concepts and providing tutorials
- Answering general knowledge questions
- Skill development and practice
AI is NOT appropriate for:
Decisions Affecting People
- Hiring, firing, or promotion decisions
- Performance evaluations
- Customer credit decisions
- Any decision requiring human judgment about individuals
Legal and Compliance
- Creating legally binding documents
- Compliance certifications
- Regulatory filings
- Legal advice
Financial Reporting
- Financial statements
- Investor communications
- Audit materials
Safety-Critical Functions
- Any output where errors could cause physical harm
- Medical advice or diagnosis
- Security-critical systems
Customer-Facing Without Review
- Any customer communication must have human review before sending
- Public statements require approval per existing process
Why this matters: AI excels at certain tasks and fails at others. Clear boundaries prevent misuse while enabling productive use.
Section 4: Quality Standards
AI makes mistakes. Review requirements catch them.
Template language:
Quality Review Requirements
All AI-generated content must be reviewed before use. AI outputs frequently contain:
- Factual errors and hallucinations
- Outdated information
- Inconsistent tone or messaging
- Logical flaws
- Bias
Review levels by content type:
Level 1: Self-review Applies to: Internal drafts, personal productivity use Requirement: User reviews for accuracy and appropriateness before use
Level 2: Peer review Applies to: Internal communications to groups, process documentation Requirement: Another person reviews before distribution
Level 3: Manager review Applies to: External communications, customer-facing content Requirement: Manager or designated reviewer approves before use
Level 4: Subject matter expert review Applies to: Technical content, legal-adjacent content, compliance-related materials Requirement: SME verifies accuracy before use
Fact-checking requirements:
- All statistics and claims must be verified against original sources
- AI-cited sources must be confirmed to exist and contain claimed information
- Dates, names, and specific facts must be verified
- Links must be tested before inclusion
What to look for in review:
- Accuracy of facts and claims
- Appropriateness of tone
- Compliance with brand guidelines
- Logical consistency
- Potential bias or sensitivity issues
Implementation note: These review levels should match your existing content approval processes where possible. The goal is integration, not creation of parallel systems.
Section 5: Disclosure Requirements
When must AI use be disclosed? This is evolving terrain.
Template language:
AI Disclosure Requirements
External disclosure required:
- Public-facing content that is primarily AI-generated
- Customer communications where AI generated the response
- Creative work submitted to clients (if AI substantively involved)
- Situations where customers or stakeholders would reasonably expect to know
External disclosure not required:
- AI used as a drafting tool with significant human editing
- AI used for research, brainstorming, or internal preparation
- Standard productivity use where AI assists but human produces final output
Disclosure format: When disclosure is required, use clear language such as:
- “This content was created with AI assistance”
- “AI was used in the development of this [document/content/analysis]”
Internal documentation: Maintain records of significant AI use for:
- Content creation for important documents
- Analysis informing major decisions
- Any use where audit trail might be needed
Regulatory considerations: Industry-specific regulations may require additional disclosure. Consult with [Legal/Compliance] for requirements in regulated functions.
Why this matters: Disclosure expectations are evolving. Having a clear policy provides defensible positions and demonstrates responsibility.
Section 6: Governance and Accountability
Who owns AI policy and how is it enforced?
Template language:
Governance Structure
Policy ownership: This policy is owned by [IT/Legal/designated function] with input from [stakeholders].
Updates and revisions: This policy will be reviewed [quarterly/semi-annually] and updated as:
- AI capabilities change
- Regulatory requirements evolve
- Business needs shift
- Issues or gaps are identified
Training requirements: All employees must complete AI policy training within [30 days] of hire and [annually] thereafter. Training includes:
- Policy overview
- Data classification rules
- Practical scenarios
- Acknowledgment of understanding
Violation handling: Violations of this policy will be addressed through existing disciplinary processes. Intentional violation of data rules may result in immediate termination.
Questions and reporting:
- Questions about this policy: [contact]
- Report potential violations: [contact/process]
- Request exceptions: [contact/process]
Exception process: Exceptions to this policy require written approval from [designated authority]. Document the exception, rationale, and any mitigating controls.
Implementation Checklist
Before rolling out your policy:
Legal review:
- General counsel has reviewed policy
- Compliance has reviewed for regulatory requirements
- Industry-specific requirements addressed
Technical alignment:
- IT security has approved tool list
- Data classification aligns with existing schemes
- Technical controls support policy enforcement
Communication plan:
- All employees notified of policy
- Training materials developed
- FAQ document created
- Manager briefing completed
Support structure:
- Question contact identified
- Exception process documented
- Violation reporting mechanism established
Documentation:
- Policy posted in accessible location
- Version control established
- Review schedule set
Adapting This Template
This template needs customization for your specific situation.
Industry-specific additions:
Healthcare: Add HIPAA-specific requirements, PHI handling rules Financial Services: Add SEC, FINRA, or banking regulation requirements Legal: Add privilege protection, conflict checking requirements Government: Add security clearance and classification requirements
Size-based modifications:
Small companies: Simplify governance structure, combine roles Large enterprises: Add cross-functional review committees, detailed approval chains Startups: Focus on data protection basics, expand governance as you grow
Risk tolerance:
Conservative: Tighter restrictions, more review requirements Aggressive: Faster approval paths, broader permitted uses Balanced: Tiered approach based on risk level
Common Policy Mistakes
Too restrictive. Policies that prohibit all AI use push people to shadow AI. Better to enable safe use than drive it underground.
Too vague. “Use AI responsibly” isn’t guidance. Specific rules help people make decisions.
Not updated. AI changes fast. Policies written in 2023 may not fit 2025 tools and capabilities. Build in review cycles.
No training. A policy people don’t understand doesn’t protect you. Training matters.
No enforcement. Policies without consequences become suggestions. Decide how you’ll handle violations.
Ignoring shadow AI. Pretending people don’t use personal AI accounts doesn’t make it true. Address the reality.
According to EY research, executives have found success creating three governance protocols for varying levels of risk per use case. This tiered approach allows innovation while maintaining appropriate controls.
Getting Started
Building your AI policy:
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Audit current state - What AI are people already using? What data might be exposed?
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Define priorities - What risks matter most? Data protection? Quality? Compliance?
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Draft policy - Use this template as a starting point, customize for your situation
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Get reviews - Legal, IT, compliance, HR, and business stakeholders
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Develop training - Policy is useless without understanding (see our AI training guide)
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Communicate broadly - All-hands announcement, manager cascades, written distribution
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Provide support - Answer questions, handle exceptions, address confusion
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Enforce consistently - Policy without enforcement is suggestion
-
Review regularly - Quarterly minimum in fast-moving AI space
An AI policy isn’t about restricting your team. It’s about enabling them to use AI productively while managing real risks. Get the balance right, and you unlock AI benefits without the downside.
For help measuring whether your AI initiatives are working, see our measuring AI ROI guide.
