Generative AI Policy Template for Enterprises

Q: What should a generative AI policy include?

A comprehensive generative AI policy should include: approved tools and platforms, acceptable use guidelines, data classification rules, intellectual property considerations, security requirements, human review requirements, training programs, compliance enforcement procedures, and governance structures.

Why You Need a Generative AI Policy

The absence of formal generative AI policies creates four critical risk categories that every organization must address:

1. Data Exposure and Confidentiality Breaches

Samsung Semiconductor Code Leak (April 2023)

Samsung engineers used ChatGPT to optimize proprietary source code, debug semiconductor equipment code, and transcribe confidential meetings. These inputs became part of ChatGPT's training data. Samsung banned ChatGPT enterprise-wide and deployed private LLM infrastructure within months.^[3]

When employees lack approved tools, they use whatever's available. A 2024 survey found 45% of employees use unauthorized AI tools, with most unaware that free versions of ChatGPT, Claude, and Gemini use inputs for model training unless users opt out.^[2]

2. Intellectual Property Contamination

AI-generated content creates murky IP ownership questions. The U.S. Copyright Office maintains that only human-created works qualify for copyright protection, meaning purely AI-generated content may not be copyrightable. For organizations selling software or creative works, this creates massive risk.^[7]

Additionally, AI systems trained on copyrighted material face litigation. The New York Times sued OpenAI and Microsoft for $billions over unauthorized training on NYT content. Getty Images sued Stability AI. Authors and artists have filed class actions. Organizations using these tools inherit potential vicarious liability.^[8]

3. Regulatory and Compliance Violations

Sector-specific regulations create AI-specific compliance obligations:

Regulatory Requirements by Sector

Sector	Regulation	AI-Specific Requirements
Healthcare	HIPAA	Business Associate Agreements (BAAs) required for any AI processing PHI; many consumer AI tools lack BAAs
Financial Services	GLBA, FCRA, ECOA	Fair lending laws prohibit algorithmic discrimination; explainability required for adverse actions
EU Operations	EU AI Act	Risk assessments, conformity declarations, quality management systems for high-risk AI (Aug 2026)
Colorado (US)	Colorado AI Act	Impact assessments, consumer notice, opt-out rights for high-risk AI decisions (June 2026)

4. AI Hallucinations and Malpractice Liability

Mata v. Avianca (May 2023)

Attorney Steven Schwartz used ChatGPT to research case citations. ChatGPT hallucinated six fake cases with realistic-sounding names, docket numbers, and quotes. Schwartz filed them with federal court. The judge sanctioned him for filing false documents, calling it "unprecedented" legal malpractice.^[4]

In legal services, 68% of professionals cite hallucinations as their top AI concern, with over 40% reporting LLM drafts requiring complete manual revision. Yet attorneys continue using AI without sufficient verification protocols.^[9]

Policy Scope & Applicability

Effective AI policies clearly define who they cover, what systems they govern, and what use cases they address.

Who Is Covered

AI policies should apply to:

All employees, contractors, and consultants with access to company systems or data
Third-party vendors processing company data through AI systems
Partners and collaborators with data-sharing agreements

What Systems Are Governed

Policies should cover:

Generative AI tools: ChatGPT, Claude, Gemini, Copilot, Midjourney, and similar systems
Code generation tools: GitHub Copilot, Cursor, Replit, Amazon CodeWhisperer
AI-powered business tools: Sales assistants, customer service bots, marketing content generators
Third-party AI integrations: Plugins, APIs, or embedded AI in SaaS platforms

What Use Cases Are Addressed

Policies should distinguish between:

Permitted Use Cases

Draft internal documentation
Research and learning
Code assistance (non-production)
Content brainstorming

Prohibited Use Cases

Processing customer PII
Generating legal/medical advice
Automated decision-making
Training on proprietary code

Approved Tools & Platforms

Organizations should maintain a whitelist of approved AI tools that meet security, privacy, and compliance requirements.

Whitelist Approach

The whitelist model provides several advantages:

Centralized procurement: Negotiate enterprise agreements with better pricing, security terms, and data protections
Security vetting: Conduct vendor security assessments before organization-wide deployment
Usage monitoring: Track adoption, costs, and potential misuse through centralized billing
Compliance alignment: Ensure tools meet regulatory requirements (BAAs for HIPAA, data localization for GDPR)

Procurement Requirements

Before approving any AI tool, require vendor documentation including:

Data Processing Agreement (DPA)
GDPR-compliant terms specifying data handling, retention, deletion procedures
Business Associate Agreement (BAA)
Required for healthcare organizations processing PHI (HIPAA requirement)
Security Questionnaire / SOC 2 Report
Independent validation of security controls, access management, encryption
Training Data Transparency
Disclosure of whether user inputs train models and how to opt out

Acceptable Use Guidelines

Clear acceptable use guidelines prevent the most common policy violations while enabling productive AI adoption.

What's Allowed

Permitted Activities

Research and exploration: Learning how AI tools work, understanding capabilities and limitations
Draft creation: Initial drafts of internal documentation, emails, presentations (subject to human review)
Code assistance: Syntax help, debugging suggestions, code explanation (for approved development tools only)
Data analysis: Analyzing anonymized, non-confidential datasets for insights
Translation and summarization: Translating public content or summarizing non-confidential documents
Creative brainstorming: Generating ideas, concepts, or creative alternatives

What's Prohibited

Prohibited Activities

Confidential information: Inputting trade secrets, source code, proprietary algorithms, customer lists, or strategic plans
Personal data: Processing PII, PHI, financial data, or other regulated data without approved safeguards
Legal/medical advice: Using AI to generate legal opinions, medical diagnoses, or professional advice
Automated decision-making: Using AI outputs for employment, lending, insurance, or other consequential decisions without human review
Bypassing security controls: Using personal accounts to circumvent organizational AI restrictions
Plagiarism or misrepresentation: Presenting AI-generated content as original human work without disclosure
Harmful content generation: Creating discriminatory, defamatory, or illegal content

Data Classification & Handling

Organizations should implement a data classification framework that governs what data can be processed through AI systems.

Four-Tier Classification Model

Data Classification for AI Use

Classification	Definition	AI Use Permitted	Examples
Public	Publicly available information	Yes (any approved tool)	Published blog posts, marketing materials, press releases
Internal	Non-public but non-sensitive	Limited (enterprise tools only)	Meeting notes, project plans, internal wikis
Confidential	Business-sensitive information	Prohibited (except approved private deployments)	Source code, customer lists, financials, roadmaps
Restricted	Regulated or legally protected	Prohibited (exception requires legal approval)	PII, PHI, payment data, MNPI

Special Handling for Regulated Data

Organizations in regulated industries must implement additional controls:

Healthcare (HIPAA)

All AI tools processing PHI require signed BAAs. Free consumer AI tools (ChatGPT, Claude free tier, Gemini) do not offer BAAs and are prohibited for any patient data. Enterprise healthcare AI must use on-premise or private cloud deployments.^[10]

Financial Services

GLBA, FCRA, and fair lending laws prohibit discrimination in credit decisions. Any AI used for lending, insurance pricing, or account management must undergo bias testing. Model explainability required for adverse action notices under FCRA.^[11]

EU/International Operations

GDPR requires DPAs for all vendors processing EU personal data. The EU AI Act (August 2026) requires conformity assessments, risk management, and quality management systems for high-risk AI. Data residency requirements may mandate EU-hosted models.^[12]

Intellectual Property Considerations

AI-generated content creates complex IP ownership questions that organizations must address proactively.

Copyright and Ownership

The U.S. Copyright Office maintains that only works created by humans are copyrightable. AI-generated content without substantial human authorship may lack copyright protection, creating risk for organizations selling software, content, or creative works.^[7]

Copyright Office Guidance (March 2023)

The Copyright Office clarified that works generated entirely by AI without human creative input are not copyrightable. However, works where humans select, arrange, or modify AI outputs with creative judgment may qualify. Organizations must document human involvement to preserve IP rights.^[7]

Licensing and Third-Party IP

AI systems trained on copyrighted material face ongoing litigation. Organizations must assess exposure:

Training data lawsuits: NYT vs. OpenAI, Getty vs. Stability AI, Authors Guild class actions all allege unauthorized use of copyrighted training data^[8]
Output similarity: AI tools may reproduce copyrighted material verbatim, exposing users to infringement claims
Vendor indemnification: Most AI providers disclaim liability for IP infringement in their ToS, placing risk on users

Policy Recommendations

Require human authorship: All AI-generated content must undergo substantial human review, editing, and creative input to preserve copyright eligibility
Mandate disclosure: Customer-facing content must disclose AI involvement where legally required or when material to the transaction
Prohibit code copying: Ban directly copying AI-generated code into production without license verification and security review
Document AI use: Maintain records of which content used AI assistance to support copyright registration or defend infringement claims

Security Requirements

AI tools introduce unique security risks beyond traditional SaaS applications. Policies must address authentication, access controls, data retention, and monitoring.

Authentication and Access Control

SSO integration: All enterprise AI tools must support SAML or OIDC single sign-on
MFA enforcement: Multi-factor authentication required for all AI tool access
Role-based access: Limit access to AI tools based on job function and data classification clearance
Personal account prohibition: Ban use of personal AI accounts (personal Gmail ChatGPT, etc.) for work purposes

Data Retention and Deletion

Opt out of training: Disable model training on user inputs for all approved enterprise tools
Conversation history limits: Configure tools to delete conversation history after 30/60/90 days based on data sensitivity
Data residency: Ensure data processing occurs in approved geographic regions (critical for GDPR, China data laws)
Right to deletion: Vendors must provide mechanism to delete user data upon request within 30 days

Logging and Monitoring

Enterprise AI deployments should implement comprehensive logging:

Audit trails: Log all AI queries, responses, user IDs, timestamps, and data classifications for forensic analysis
Anomaly detection: Alert on unusual usage patterns (bulk queries, off-hours access, data exfiltration attempts)
DLP integration: Integrate with Data Loss Prevention (DLP) tools to block PII, secrets, or credentials in AI inputs

Human Review Requirements

The NIST AI Risk Management Framework and emerging AI regulations emphasize "human in the loop" oversight for consequential decisions. Organizations must define when human review is mandatory.^[13]

Mandatory Human Review Scenarios

When Human Review Is Required

Use Case	Review Requirement	Rationale
Legal filings/opinions	100% attorney review + fact verification	AI hallucinations create malpractice liability (Mata v. Avianca)^[4]
Medical advice/diagnoses	Licensed clinician review + liability documentation	HIPAA liability, medical malpractice, patient safety
Employment decisions	HR professional review + bias audit	EEOC anti-discrimination requirements
Credit/lending decisions	Qualified reviewer + adverse action explanation	FCRA, ECOA fair lending laws
Customer-facing content	Subject matter expert approval	Brand accuracy, legal disclaimers, customer trust
Production code	Security review + testing before deployment	Code quality, security vulnerabilities, license compliance

Verification Standards

Human reviewers must be trained to:

Verify factual claims: Check citations, statistics, case law references against original sources
Assess bias and fairness: Evaluate outputs for discriminatory language, stereotypes, or disparate impact
Check brand/voice alignment: Ensure content matches organizational standards, tone, and policies
Document review process: Maintain audit trail showing who reviewed, what changed, and approval timestamp

Training & Awareness

Effective AI policies require comprehensive training programs to ensure employees understand rules, risks, and approved workflows.

Required Training Components

Initial Onboarding (All Employees)

Overview of organizational AI policy
Approved vs. prohibited tools
Data classification and handling rules
How to request new AI tool approvals
Reporting suspected policy violations

Role-Specific Training

Developers: Secure coding with AI assistants, license compliance, security testing
Legal/Finance: Hallucination verification, citation checking, professional liability
Healthcare: HIPAA requirements, BAA verification, patient data protections
Marketing/Sales: Brand guidelines, IP ownership, customer disclosure requirements
Managers: Monitoring team AI use, escalation procedures, policy enforcement

Ongoing Awareness

Quarterly policy updates as new tools/regulations emerge
Case studies of AI incidents (Samsung breach, Mata v. Avianca)
New tool announcements and training
Annual policy recertification

Compliance & Enforcement

Policies without enforcement mechanisms fail. Organizations must define violations, consequences, and escalation procedures.

Violation Categories

Critical Violations (Immediate Investigation)

Processing restricted data (PII, PHI, payment data) through unauthorized tools
Intentionally bypassing security controls or data loss prevention systems
Exposing trade secrets, source code, or confidential business information
Using AI for illegal, discriminatory, or harmful purposes

Moderate Violations (Manager Review)

Using non-approved AI tools without malicious intent
Processing confidential (but not restricted) data without proper safeguards
Failing to disclose AI use when required
Skipping required human review processes

Minor Violations (Training/Warning)

Using approved tools for unapproved use cases due to lack of awareness
Incomplete documentation of AI-generated content
Delayed compliance with new policy updates

Progressive Discipline Framework

Consequences should be proportional to violation severity:

First minor violation: Documented verbal warning + mandatory retraining
Second minor or first moderate: Written warning + performance plan
Repeated moderate violations: Suspension + final written warning
Critical violation: Immediate suspension pending investigation; termination for cause if substantiated

Policy Governance

AI technology evolves rapidly. Policies require regular review, clear ownership, and stakeholder input to remain effective.

Governance Structure

Policy Owner: Chief Information Security Officer (CISO) or Chief Compliance Officer maintains and updates policy
AI Governance Committee: Cross-functional team (Legal, Security, IT, Business, HR) reviews quarterly
Executive Sponsor: C-level executive (CTO, CIO, General Counsel) approves major policy changes

Review Cycles

Quarterly reviews: Assess new AI tools, regulatory changes, incident learnings
Annual comprehensive review: Full policy refresh with stakeholder input
Emergency updates: Triggered by critical security incidents, regulatory actions, or major vendor changes

Complete Policy Template

The following template provides copy-paste ready sections. Customize based on your organization's size, industry, and regulatory requirements.

Generative AI Acceptable Use Policy

GENERATIVE AI ACCEPTABLE USE POLICY Policy Owner: [Chief Information Security Officer] Effective Date: [Date] Last Reviewed: [Date] Version: 1.0 1. PURPOSE This policy establishes requirements for the acceptable use of generative artificial intelligence (AI) tools within [ORGANIZATION NAME]. It aims to enable productive AI adoption while protecting confidential information, ensuring compliance with applicable laws, mitigating security risks, and maintaining intellectual property rights. 2. SCOPE This policy applies to: • All employees, contractors, consultants, and temporary workers • All generative AI tools and services accessed using organization resources or for organization business • All data and content created, processed, or transmitted using AI systems 3. APPROVED TOOLS The following AI tools have been approved for use at [ORGANIZATION]: Enterprise Tools (Confidential Data Permitted): • [Tool 1] - Use cases: [Description] • [Tool 2] - Use cases: [Description] General Tools (Public/Internal Data Only): • [Tool 3] - Use cases: [Description] Request approval for new AI tools via [PROCESS]. 4. ACCEPTABLE USE PERMITTED activities include: ✓ Research, learning, and skill development ✓ Drafting internal documentation (subject to review) ✓ Code assistance for approved development tools ✓ Analyzing anonymized, non-confidential data ✓ Translating or summarizing public content ✓ Creative brainstorming and ideation PROHIBITED activities include: ✗ Processing confidential or restricted data without approved safeguards ✗ Inputting PII, PHI, financial data, or trade secrets into unapproved tools ✗ Using AI to generate legal opinions, medical diagnoses, or professional advice ✗ Automated decision-making for employment, lending, or other consequential decisions ✗ Bypassing security controls or using personal AI accounts for work ✗ Presenting AI content as original human work without disclosure ✗ Generating discriminatory, defamatory, or illegal content 5. DATA CLASSIFICATION AI use must comply with data classification standards: PUBLIC: Any approved AI tool permitted INTERNAL: Enterprise AI tools only CONFIDENTIAL: Prohibited except approved private deployments RESTRICTED (PII/PHI/Financial): Prohibited without legal/compliance approval 6. SECURITY REQUIREMENTS • Single Sign-On (SSO) and multi-factor authentication (MFA) mandatory • Personal AI accounts prohibited for organization business • Disable model training on organization inputs • Configure conversation history retention per data classification • Report security incidents to [SECURITY TEAM] within [X] hours 7. INTELLECTUAL PROPERTY • AI-generated content must undergo substantial human review and editing • Document which content used AI assistance • Verify AI-generated code does not violate licenses before production use • Disclose AI involvement in customer-facing content where required by law 8. HUMAN REVIEW REQUIREMENTS Mandatory human review for: • Legal filings, contracts, or legal opinions • Medical advice or clinical documentation • Employment, lending, or insurance decisions • Customer-facing content before publication • Production code before deployment 9. TRAINING • All employees complete AI policy training within [X] days of hire • Role-specific training for high-risk functions (legal, healthcare, finance) • Annual recertification required • Quarterly updates on new tools and regulations 10. VIOLATIONS AND ENFORCEMENT Critical violations (restricted data exposure, intentional security bypass): → Immediate investigation; potential termination Moderate violations (unapproved tools, missing human review): → Written warning + performance plan Minor violations (lack of awareness): → Documented verbal warning + retraining 11. GOVERNANCE • Policy Owner: [CISO / Chief Compliance Officer] • Governance Committee: [Cross-functional team] • Quarterly policy reviews • Annual comprehensive refresh • Emergency updates as needed 12. EXCEPTIONS Requests for policy exceptions must be submitted to [GOVERNANCE COMMITTEE] with business justification, risk assessment, and proposed compensating controls. 13. RELATED POLICIES • Data Classification Policy • Information Security Policy • Intellectual Property Policy • [Industry-specific]: HIPAA Privacy Policy / PCI DSS Compliance / etc. 14. QUESTIONS Contact [[email protected]] with questions or to report violations. ACKNOWLEDGMENT I have read, understood, and agree to comply with this Generative AI Acceptable Use Policy. Employee Name: ___________________________ Signature: ________________________________ Date: ____________________________________

Customize this template based on your organization's specific regulatory requirements, risk tolerance, and approved tool list. Consult legal counsel before implementation.

Sector-Specific Addendums

Organizations in regulated industries should add sector-specific provisions:

Healthcare Addendum (HIPAA)

All AI tools processing Protected Health Information (PHI) must: • Have a signed Business Associate Agreement (BAA) • Use encryption at rest and in transit (AES-256 or equivalent) • Implement access controls limiting PHI to minimum necessary • Provide audit logs of all PHI access for 6 years • Support patient rights to access, amend, and delete PHI • Report breaches affecting 500+ individuals to HHS within 60 days Consumer AI tools (ChatGPT, Claude, Gemini free tiers) do NOT offer BAAs and are PROHIBITED for any patient data.

Financial Services Addendum (GLBA/FCRA)

AI systems used for credit, lending, or insurance decisions must: • Undergo bias testing across protected classes (race, gender, age, etc.) • Provide explainability for adverse action notices (FCRA requirement) • Maintain model documentation including training data, features, performance • Implement human review for all automated credit decisions • Comply with fair lending laws (ECOA, FHA, state equivalents) • Report algorithmic discrimination testing to board annually

GLACIS Framework

From Policy to Evidence

Policies define what should happen. Evidence proves what actually happened. Most organizations have the former but lack the latter—creating a "proof gap" that regulators and customers will scrutinize.

The Problem with Documentation-Only Governance

Traditional compliance relies on policies, procedures, and self-attestations. But when regulators investigate or customers audit, they ask: "Prove your controls executed correctly." Documentation can't answer that question. Evidence can.

GLACIS Evidence Infrastructure

GLACIS generates cryptographic evidence that your AI controls executed as designed—human-in-loop reviews occurred, bias checks ran, PII redaction worked. Third parties can independently verify this evidence without accessing your systems. It's the difference between claiming governance and proving governance.

Learn About GLACIS Evidence Generation

Frequently Asked Questions

What should a generative AI policy include?

A comprehensive policy should cover: approved tools and platforms, acceptable use guidelines, data classification rules, intellectual property considerations, security requirements, human review requirements, training programs, compliance enforcement procedures, and governance structures with clear ownership and review cycles.

How long does it take to develop an AI policy?

The average enterprise AI policy takes 3 months to develop, requiring input from legal, security, compliance, IT, and business stakeholders. Organizations can accelerate this timeline by starting with a template (like the one in this guide) and customizing for specific regulatory requirements and risk tolerance.

What percentage of companies have formal AI policies?

Only 30% of organizations have formal generative AI policies in place, despite 70% of employees reporting use of AI tools in their work. This policy gap creates significant risk exposure, with 45% of employees using unauthorized shadow AI tools that may expose confidential data or create compliance violations.

Do I need different AI policies for different departments?

Most organizations benefit from a single enterprise-wide policy with department-specific addendums. For example, legal teams may need stricter confidentiality rules, while customer service may require specific customer data protections. Healthcare and financial services require sector-specific compliance provisions (HIPAA, GLBA, etc.).

How do I enforce an AI policy?

Effective enforcement requires: mandatory training for all employees, technical controls (SSO, DLP integration, monitoring), regular audits of AI usage logs, clear violation categories with progressive discipline, and executive support. Consider appointing AI champions within each department to promote compliance and answer questions.

Should I ban ChatGPT entirely?

Outright bans often backfire by driving usage underground. Instead, approve enterprise versions with proper security controls (ChatGPT Enterprise, Claude for Work, etc.) while prohibiting personal accounts. Provide approved tools that meet employees' needs—if you don't, they'll use shadow AI regardless of policy.

References

Gartner Research. "AI Policy Adoption Survey 2024." gartner.com
Salesforce. "Global AI Survey: Shadow AI Usage." 2024. salesforce.com
Bloomberg. "Samsung Bans ChatGPT After Code Leak." April 2023. bloomberg.com
Mata v. Avianca, Inc., No. 22-cv-1461 (PKC), 2023 WL 4114965 (S.D.N.Y. May 27, 2023). law.justia.com
Deloitte. "Enterprise AI Governance Study." 2024. Policy development timelines analysis.
NIST AI Risk Management Framework analysis of policy components. nist.gov
U.S. Copyright Office. "Copyright Registration Guidance: Works Containing Material Generated by Artificial Intelligence." March 2023. copyright.gov
New York Times Co. v. OpenAI Inc., Case No. 1:23-cv-11195 (S.D.N.Y. Dec. 27, 2023); Getty Images v. Stability AI, Case No. 1:23-cv-00135 (D. Del. Feb. 3, 2023)
Thomson Reuters. "Legal Professional AI Survey 2024." thomsonreuters.com
U.S. Department of Health & Human Services. "HIPAA Business Associate Agreements." hhs.gov
Consumer Financial Protection Bureau. "Fair Lending and AI." consumerfinance.gov
European Commission. "EU AI Act: High-Risk AI Systems." digital-strategy.ec.europa.eu
NIST. "AI Risk Management Framework (AI RMF 1.0)." January 2023. nist.gov

Generative AI Policy Template

Executive Summary

In This Guide