AI Policy Template: What Every Section Should Include

A comprehensive AI usage policy provides the foundation for responsible AI governance. Here's a detailed breakdown of what to include in each section, with practical examples and templates you can adapt.

1. Purpose and Scope

What to Include

Policy Objectives:

• Why this policy exists
• What it aims to achieve
• How it supports organizational goals
• Connection to regulatory compliance (EU AI Act, GDPR)

Scope Coverage:

• What AI systems are covered
• Who must follow the policy
• What activities are governed
• Any exclusions or special cases

Example Text

Purpose: This AI Usage Policy establishes guidelines for the responsible development, procurement, and use of artificial intelligence systems within [Company Name]. The policy aims to:

• Ensure compliance with the EU AI Act, GDPR, and related regulations
• Protect the rights and interests of employees, customers, and other stakeholders
• Mitigate risks associated with AI systems
• Foster innovation while maintaining ethical standards
• Establish clear accountability for AI-related decisions

Scope: This policy applies to:

• All employees, contractors, and third parties working on behalf of [Company Name]
• All AI systems used in our operations, products, or services
• All stages of the AI lifecycle: development, procurement, deployment, monitoring, and decommissioning

This includes but is not limited to:

• Third-party AI services (SaaS, APIs, cloud AI)
• Custom-developed AI systems
• AI components embedded in other systems
• Open-source AI models and tools

2. Definitions

What to Include

Clear definitions of key terms so everyone understands policy requirements the same way.

Essential Definitions

Artificial Intelligence (AI) System: Machine-based system that, for explicit or implicit objectives, infers from input received how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.

High-Risk AI System: AI system listed in AI Act Annex III or meeting specific risk criteria, including AI used for:

• Employment and worker management
• Access to education and vocational training
• Access to essential services
• Law enforcement
• Migration and border control
• Administration of justice

Provider: Organization that develops an AI system or has it developed, and places it on the market or puts it into service under its own name or trademark.

Deployer: Organization that uses an AI system under its own authority, except where the use is for personal non-professional activity.

Personal Data: Any information relating to an identified or identifiable natural person.

AI Incident: Malfunction, failure, or unintended behavior of an AI system that causes or could cause harm, bias, or rights violations.

AI Literacy: Skills and knowledge enabling proper understanding and use of AI systems, including awareness of opportunities and risks.

3. Roles and Responsibilities

What to Include

Clear assignment of AI governance duties across the organization.

Key Roles Template

AI Governance Board

• Composition: [CEO/Senior leadership, Legal, IT, Privacy Officer, relevant department heads]
• Responsibilities:
  • Approve high-risk AI system deployments
  • Review and update AI policy
  • Oversee AI compliance program
  • Make decisions on significant AI incidents
  • Allocate resources for AI governance

AI Officer / Coordinator

• Position: [Title, e.g., Chief Technology Officer, IT Director]
• Responsibilities:
  • Day-to-day policy implementation
  • Maintain AI system inventory
  • Coordinate risk assessments
  • Manage approval workflows
  • Liaise with vendors and authorities
  • Coordinate training program
  • Incident response coordination

Data Protection Officer

• Responsibilities:
  • Ensure AI use complies with GDPR
  • Conduct or oversee DPIAs
  • Advise on data protection aspects of AI
  • Handle data subject requests related to AI
  • Monitor AI data processing activities

Department Managers

• Responsibilities:
  • Ensure team compliance with policy
  • Identify new AI use cases in their area
  • Provide input for risk assessments
  • Oversee human oversight of AI in their function
  • Report AI issues promptly

All Employees

• Responsibilities:
  • Follow this policy in daily work
  • Complete required AI literacy training
  • Use only approved AI tools
  • Report AI incidents or concerns
  • Protect sensitive information from unauthorized AI use

4. AI System Classification and Risk Management

What to Include

Process for categorizing AI systems by risk level and corresponding controls.

Classification Framework

Minimal Risk AI

• Examples: Spam filters, inventory optimization, basic analytics
• Requirements: General security and data protection practices
• Approval: Manager approval, logged in AI inventory

Transparency-Required AI

• Examples: Customer-facing chatbots, AI-generated marketing content
• Requirements:
  • User disclosure of AI use
  • Clear labeling of AI-generated content
  • Easy access to human alternatives
• Approval: Department head + AI Officer

High-Risk AI

• Examples: Hiring algorithms, creditworthiness assessment, access to essential services
• Requirements:
  • Full conformity assessment
  • Data Protection Impact Assessment
  • Fundamental rights impact assessment
  • Human oversight mechanism
  • Detailed documentation
  • CE marking verification (if applicable)
  • Registration in EU database
• Approval: AI Governance Board

Prohibited AI

• Examples: Social scoring, manipulative AI, mass surveillance, biometric categorization of sensitive attributes
• Status: NOT PERMITTED under any circumstances
• Action: Immediately discontinue if discovered

Risk Assessment Process

Before deploying any AI system:

1. Identify the AI system and its intended use
2. Classify into risk category using decision tree (see Annex)
3. Assess specific risks based on classification
4. Document assessment in AI Use Case Register
5. Implement required controls for that category
6. Obtain necessary approvals
7. Monitor ongoing performance and compliance

5. Approved and Prohibited Uses

What to Include

Specific guidance on acceptable and unacceptable AI applications.

Approved Uses (Examples)

✅ Content Creation Support

• AI-assisted drafting of marketing copy (with human review)
• Grammar and style checking
• Translation of non-sensitive content
• Generating images for internal presentations

✅ Data Analysis

• Customer behavior analytics (anonymized)
• Sales forecasting
• Market trend analysis
• Quality control automation

✅ Productivity Tools

• Meeting transcription and summarization
• Email categorization and prioritization
• Code completion for software development
• Document search and retrieval

✅ Customer Interaction

• Chatbots for basic customer service inquiries (with disclosure and human escalation)
• Personalized product recommendations
• Automated appointment scheduling

Prohibited Uses

❌ Personal Data Misuse

• Uploading customer personal data to public AI tools
• Processing special category data (health, biometric, etc.) without proper safeguards
• Using AI to infer sensitive personal attributes

❌ Manipulative Practices

• AI that exploits vulnerabilities (children, disabilities)
• Subliminal manipulation techniques
• Deceptive AI interactions (undisclosed AI use)

❌ Discrimination Risk

• AI for hiring/HR without bias testing and human oversight
• Creditworthiness assessment without transparency and review rights
• Any AI shown to discriminate against protected characteristics

❌ Privacy Violations

• Facial recognition or biometric surveillance without authorization
• Monitoring employees beyond what's legally permitted
• Processing personal data without valid legal basis

❌ Security Risks

• Using AI to generate or spread misinformation about the company
• Uploading source code or trade secrets to public AI
• Deploying AI with known security vulnerabilities

6. Data Handling and Privacy

What to Include

How personal data must be managed in AI systems, integrating GDPR requirements.

Data Protection Principles for AI

1. Lawfulness and Transparency

• Identify legal basis before AI processing (consent, contract, legitimate interest)
• Update privacy notices to explain AI use
• Provide clear information about automated decision-making

2. Purpose Limitation

• Use personal data only for specified, explicit purposes
• Don't repurpose training data without new legal basis
• Define AI system's purpose clearly before deployment

3. Data Minimization

• Process only data necessary for AI's function
• Anonymize or pseudonymize where possible
• Remove unnecessary fields before AI processing

4. Accuracy

• Ensure training data is accurate and up-to-date
• Implement processes to correct AI-related errors
• Enable data subjects to challenge inaccurate AI outputs

5. Storage Limitation

• Define retention periods for AI-processed data
• Delete data when purpose is fulfilled
• Special attention to AI system logs and outputs

6. Security

• Encrypt personal data used in AI
• Control access to AI systems processing personal data
• Protect against unauthorized or unlawful processing
• Test AI systems for data security vulnerabilities

Data Protection Impact Assessments

Required before deploying AI that involves:

• Large-scale processing of sensitive personal data
• Systematic monitoring of publicly accessible areas
• Automated decision-making with significant effects
• Profiling that could lead to discrimination

DPIA Process:

1. Describe AI processing operations
2. Assess necessity and proportionality
3. Identify data protection risks
4. Design mitigation measures
5. Document assessment
6. Review by Data Protection Officer
7. Consult supervisory authority if high residual risk

Individual Rights

Ensure processes to handle:

• Right to Information: Explain AI processing in privacy notices
• Right of Access: Provide information about AI decisions affecting the individual
• Right to Human Review: Enable review of solely automated decisions
• Right to Object: Allow objection to profiling/automated decisions
• Right to Erasure: Delete personal data from AI systems when required

7. Human Oversight and Decision-Making

What to Include

Requirements for meaningful human control over AI systems, especially high-risk AI.

Human Oversight Framework

For All AI Systems:

• Qualified person designated as responsible for monitoring
• Clear escalation path for issues
• Authority to override or disconnect AI if needed

For High-Risk AI Systems:

1. Oversight Capabilities:

• Humans must be able to:
  • Fully understand AI capabilities and limitations
  • Monitor AI operation appropriately
  • Interpret AI outputs correctly
  • Decide when not to use AI or override outputs
  • Intervene in or interrupt AI operation

2. Human-in-the-Loop (HITL):

• AI decision is a recommendation only
• Human makes final decision
• Human can override AI suggestion

Example: AI screens job applications but doesn't automatically reject anyone; recruiter reviews AI recommendations and makes final decisions.

3. Human-on-the-Loop (HOTL):

• AI makes decisions autonomously
• Human monitors for issues
• Human can intervene if problems detected

Example: AI chatbot handles customer service, human monitors conversations and can take over if customer is dissatisfied or issue is complex.

4. Human-in-Command (HIC):

• Human maintains overall control
• Can deactivate or modify AI operation
• Makes deployment and configuration decisions

Example: Management decides whether to deploy a new AI tool, how to configure it, and when to discontinue use.

Oversight Requirements by Function

HR/Employment AI:

• Final hiring/firing decisions always by qualified human
• Regular review of AI recommendations for bias
• Candidates can request human review of adverse decisions

Customer Service AI:

• Easy escalation to human agent
• Complex issues routed to humans automatically
• Human review of AI interactions causing dissatisfaction

Financial/Credit AI:

• Human reviews high-impact decisions
• Clear explanation of AI factors in decision
• Individual can request and receive human review

8. Transparency and Communication

What to Include

When and how to disclose AI use to affected individuals.

Disclosure Requirements

When Using AI Chatbots or Virtual Assistants:

• Inform users they're interacting with AI at the beginning of interaction
• Provide easy way to reach human support
• Display clear indicators (visual or textual) throughout interaction

Example Notice:

"You're chatting with our AI assistant. It can help with basic questions, but a human agent is available if needed. Type 'human' at any time to connect with our team."

When Generating Content with AI:

• Label AI-generated or AI-manipulated content
• Applies to text, images, audio, and video
• Exception: Content that's obviously fictional/creative and non-harmful

Example Labels:

• "This image was created with AI assistance"
• "Content generated with AI and reviewed by [Company Name] staff"
• Watermarks or metadata for images

When Using Emotion Recognition or Biometric Categorization:

• Explicit, prominent notice before exposure
• Explain purpose and data handling
• Provide opt-out where legally required

When AI Significantly Influences Decisions About Individuals:

• Inform individuals that AI was used
• Explain logic and significance of AI processing
• Inform of right to human review
• Provide contact for questions

Internal Transparency

Within the organization:

• Maintain AI Use Case Register accessible to relevant personnel
• Document AI system capabilities and limitations
• Share known risks and incidents with affected teams
• Provide clear channels for employees to ask about AI use

9. Vendor Management and Procurement

What to Include

Due diligence process for selecting and managing AI vendors.

Vendor Assessment Checklist

Before procuring AI systems, assess:

1. Compliance and Certification

• Does vendor claim EU AI Act compliance?
• For high-risk AI: Is system CE-marked?
• Is system registered in EU database (if required)?
• Can vendor provide conformity documentation?

2. Technical Documentation

• System capabilities and limitations
• Training data sources and quality
• Known biases or failure modes
• Performance metrics and testing results
• Model architecture (if relevant)

3. Data Protection

• GDPR-compliant data processing agreement
• Data location and cross-border transfers
• Data retention and deletion practices
• Security measures and certifications
• Subprocessor information

4. Security

• Security testing and vulnerabilities
• Incident response procedures
• Access controls and authentication
• Encryption practices
• Update and patching schedule

5. Support and Maintenance

• Documentation and training provided
• Support availability and SLAs
• Update frequency and notification
• Incident reporting processes
• System monitoring capabilities

6. Contractual Protections

• Warranty of compliance with AI Act
• Liability allocation for AI failures
• Indemnification provisions
• Audit rights
• Termination and transition assistance

Ongoing Vendor Management

• Annual vendor compliance review
• Monitor vendor notifications of changes or incidents
• Track vendor security updates and apply promptly
• Maintain vendor contact list for incident response
• Review vendor compliance as regulations evolve

10. Incident Management and Reporting

What to Include

Process for identifying, responding to, and reporting AI-related incidents.

Incident Definition

An AI incident is any of:

• AI system malfunction or failure
• Security breach involving AI
• Discovery of significant bias or discrimination
• Violation of individual rights by AI
• Non-compliance with policy or regulations
• Unauthorized AI use
• Data protection violation involving AI

Incident Response Process

1. Detection and Reporting

• Anyone can report potential incidents
• Multiple reporting channels (email, hotline, manager)
• No retaliation for good-faith reports

2. Initial Assessment

• AI Officer evaluates severity within [24 hours]
• Classify incident (minor, moderate, severe, critical)
• Determine if immediate action needed (system shutdown, containment)

3. Investigation

• Gather facts: What happened, when, impact, cause
• Identify affected individuals and data
• Document findings

4. Remediation

• Correct technical issues
• Notify affected individuals if required
• Implement preventive measures
• Update systems, processes, or training

5. Reporting

• Serious incidents reported to AI Governance Board
• High-risk AI incidents: report to provider and/or authorities per AI Act
• Data breaches: report per GDPR timelines (72 hours)
• Document all incidents in incident log

Severity Classification

Critical: Immediate risk of significant harm or rights violations

• Action: Immediate shutdown, executive notification, likely authority reporting

Severe: Substantial impact or compliance violation

• Action: Prompt remediation, board notification, possible authority reporting

Moderate: Limited impact, contained issue

• Action: Standard remediation process, management notification

Minor: No immediate risk, learning opportunity

• Action: Document, address in next review cycle

11. Training and Awareness

What to Include

How employees will learn about and stay current on AI policy.

Training Program

Initial AI Literacy Training (Required for all employees):

• What is AI and how it works
• Benefits and risks of AI
• Overview of EU AI Act and GDPR relevance
• Company AI policy key points
• Approved tools and how to use them
• How to report concerns
• Duration: [2 hours, combination of e-learning and live session]
• Frequency: Upon hiring and annually

Role-Specific Training:

• Tailored scenarios for different functions
• Department managers: oversight responsibilities
• High-risk AI users: additional compliance requirements
• IT/technical staff: security and monitoring obligations

Ongoing Awareness:

• Quarterly policy updates and reminders
• Monthly tips in company newsletter
• Quick reference guides on intranet
• "AI Office Hours" for questions

Training Tracking

• Record completion in HR/learning system
• Require acknowledgment of policy understanding
• Track questions and confusion areas to improve training
• Report training compliance to leadership quarterly

12. Monitoring and Continuous Improvement

What to Include

How the organization will track AI performance and evolve practices.

Monitoring Framework

AI System Performance:

• Track accuracy, errors, and failures
• Monitor for bias or discrimination patterns
• Collect user feedback
• Review audit logs regularly
• Conduct periodic testing

Policy Compliance:

• Regular audits of AI use vs. approved list
• Spot checks of AI outputs
• Review approval workflows
• Assess training completion rates
• Track incident trends

Regulatory Changes:

• Monitor EU AI Act implementing acts and guidance
• Track enforcement decisions
• Follow industry standards development
• Participate in industry forums

Review Cycle

Quarterly:

• AI system inventory updates
• Incident review and trend analysis
• Training effectiveness assessment
• New AI tool requests

Annually:

• Full policy review and update
• Comprehensive compliance audit
• Vendor performance reviews
• Risk assessment refresh
• Training program evaluation

Triggered:

• After serious incidents
• When deploying new high-risk AI
• Upon significant regulatory changes
• Following organizational restructuring

Continuous Improvement

• Collect feedback from employees and customers
• Analyze incident root causes
• Share lessons learned across organization
• Update policy and training based on experience
• Benchmark against industry best practices

Implementing This Template

Customization Steps:

1. Replace Placeholders: Add your company name, specific roles, timelines
2. Tailor Examples: Use AI systems and scenarios from your actual operations
3. Adjust Complexity: Scale up/down based on company size and AI use
4. Integrate Existing Policies: Reference or incorporate existing IT, security, HR policies
5. Legal Review: Have counsel review for jurisdiction-specific requirements
6. Stakeholder Input: Get feedback from IT, Legal, Privacy, business units
7. Executive Approval: Obtain leadership sign-off
8. Distribute and Train: Roll out with training program
9. Make It Accessible: Post on intranet, reference in onboarding

Essential Annexes:

• Annex 1: AI Use Case Register template
• Annex 2: Risk Assessment Checklist
• Annex 3: Vendor Due Diligence Form
• Annex 4: Employee Guidelines with Examples
• Annex 5: Incident Report Form
• Annex 6: Training Acknowledgment

This template provides a comprehensive foundation. Adapt it to your organization's specific needs, culture, and AI maturity level. The goal is a practical, living document that actually guides behavior – not just a compliance artifact.