AI Policy Template

This lesson provides a comprehensive, ready-to-use AI policy template that incorporates all ISO 42001 requirements, best practices, and regulatory compliance needs including the EU AI Act.

How to Use This Template

Review: Read the entire template thoroughly
Customize: Replace bracketed placeholders with your organization's details
Adapt: Modify sections to fit your context and risk profile
Validate: Review with legal, compliance, and technical teams
Approve: Obtain executive and board approval
Communicate: Distribute to all relevant stakeholders
Implement: Put supporting processes and tools in place
Monitor: Track compliance and effectiveness
Review: Update annually or when regulations change

Complete AI Policy Template

ARTIFICIAL INTELLIGENCE POLICY

Organization: [Organization Name]

Policy Number: [POL-AI-001]

Version: 1.0

Effective Date: [Date]

Last Review Date: [Date]

Next Review Date: [Date + 1 year]

Policy Owner: Chief AI Officer / Chief Data Officer

Approved By: Board of Directors / Chief Executive Officer

1. PURPOSE AND OBJECTIVES

1.1 Purpose

This policy establishes [Organization Name]'s framework for the responsible development, deployment, and use of Artificial Intelligence (AI) systems. It ensures that our AI practices:

Align with our organizational values and mission
Meet regulatory and legal requirements
Manage AI-specific risks effectively
Build and maintain stakeholder trust
Support ethical and responsible AI innovation

1.2 Objectives

This policy aims to:

Provide clear direction for AI initiatives across the organization
Establish governance structures for AI oversight and accountability
Define requirements for the complete AI lifecycle
Ensure fairness, transparency, and explainability in AI systems
Protect privacy and security
Enable continuous improvement and learning

1.3 Strategic Alignment

AI initiatives shall align with:

Organizational strategic objectives
Risk appetite and tolerance
Regulatory compliance requirements
Stakeholder expectations
Ethical principles and values
Industry best practices

2. SCOPE AND APPLICABILITY

2.1 Scope

This policy applies to:

AI Systems:

All AI systems developed, deployed, acquired, or used by [Organization Name]
Internal AI applications
Customer-facing AI systems
Third-party AI services and products
AI systems in research and development
AI proof-of-concepts and pilots

AI Activities:

AI strategy and planning
Data collection and management for AI
AI model development and training
AI testing and validation
AI deployment and operations
AI monitoring and maintenance
AI decommissioning

Organizational Units:

All departments, business units, and subsidiaries
All geographic locations and legal entities
All employees, contractors, and temporary staff
Third-party vendors and partners involved in AI

2.2 Out of Scope

The following are excluded from this policy:

[Specify any exclusions, e.g., basic automation, rule-based systems]
[Academic research not intended for deployment]
[Personal projects not using company resources]

2.3 Relationship to Other Policies

This policy complements and should be read in conjunction with:

Information Security Policy
Data Protection and Privacy Policy
Risk Management Policy
Code of Conduct and Ethics Policy
Third-Party Management Policy
Acceptable Use Policy

3. POLICY STATEMENT

3.1 Core Commitment

[Organization Name] is committed to developing and deploying AI systems that are:

Human-Centered: AI serves human needs, respects human dignity and autonomy, and augments rather than replaces human judgment in critical decisions.

Fair and Equitable: AI treats all people fairly without unjust discrimination, with proactive measures to detect and mitigate bias.

Transparent and Explainable: AI provides appropriate explanations for decisions, with clear communication about AI use, capabilities, and limitations.

Safe and Reliable: AI is thoroughly tested before deployment and continuously monitored to ensure reliable and safe operation.

Secure: AI is protected against attacks, misuse, and unauthorized access throughout its lifecycle.

Privacy-Respecting: AI protects personal data, complies with data protection regulations, and implements privacy by design.

Accountable: AI has clear ownership, governance, and accountability structures with mechanisms for recourse.

Sustainable: AI considers environmental and societal impacts, promoting responsible resource use.

3.2 Regulatory Compliance

All AI systems shall comply with applicable laws and regulations, including:

EU Artificial Intelligence Act
General Data Protection Regulation (GDPR)
[Sector-specific regulations, e.g., financial services, healthcare]
[Local/national AI regulations]
ISO 42001 AI Management System
ISO 27001 Information Security (where applicable)

4. RESPONSIBLE AI PRINCIPLES

4.1 Human-Centered AI

Requirements:

AI shall serve human needs and enhance human capabilities
Humans shall maintain meaningful control over AI systems
Human judgment shall not be fully displaced in consequential decisions
AI shall respect human rights, autonomy, and dignity
Users shall be able to challenge AI decisions and seek human review

Implementation:

Human oversight mechanisms appropriate to risk level
Override and escalation procedures
User interface design prioritizing human understanding
Regular assessment of human-AI interaction effectiveness

4.2 Fairness and Non-Discrimination

Requirements:

AI shall not discriminate based on protected characteristics
Training data shall be representative and balanced
Fairness metrics shall be defined and monitored
Bias shall be proactively identified and mitigated
Regular fairness audits shall be conducted

Implementation:

Mandatory bias assessment during development
Fairness metrics: [demographic parity, equal opportunity, etc.]
Maximum acceptable disparity: [5%] across demographic groups
Continuous fairness monitoring in production
Quarterly fairness audits for high-risk systems

4.3 Transparency and Explainability

Requirements:

AI use shall be disclosed to affected parties
Explanations shall be provided for AI decisions when requested
AI capabilities and limitations shall be clearly communicated
AI systems shall be documented comprehensively
Stakeholders shall have access to appropriate information

Implementation:

Model cards for all production AI systems
User-facing explanations in plain language
Documentation standards and templates
Transparency reporting (annual)
Public communication about AI practices

4.4 Privacy and Data Protection

Requirements:

Data minimization: collect only necessary data
Purpose limitation: use data only for specified purposes
Storage limitation: retain data only as long as needed
Strong security controls for data protection
Privacy by design in all AI systems
Compliance with GDPR and data protection laws

Implementation:

Privacy Impact Assessments for all AI projects
Data governance framework
Access controls and encryption
Data anonymization and pseudonymization where appropriate
Regular privacy audits
Data Subject Rights procedures

4.5 Safety and Reliability

Requirements:

Thorough testing before deployment
Continuous monitoring in production
Robust error handling and graceful degradation
Incident detection and response procedures
Regular revalidation of AI systems

Implementation:

Comprehensive testing framework
Performance requirements: [specify metrics]
Monitoring dashboards and alerting
Incident response procedures
Quarterly revalidation for high-risk systems

4.6 Security

Requirements:

Protection against adversarial attacks
Secure development practices
Access controls and authentication
Encryption of data and models
Security testing and vulnerability management

Implementation:

Secure development lifecycle
Adversarial testing
Security reviews and penetration testing
Incident response plan
Security monitoring and threat intelligence

4.7 Accountability

Requirements:

Clear ownership for every AI system
Defined roles and responsibilities
Audit trails for AI decisions
Mechanisms for recourse and complaints
Regular governance reviews

Implementation:

AI system inventory with owners
Governance structure and decision rights
Comprehensive logging and audit trails
Complaint and appeal mechanisms
Quarterly governance committee meetings

4.8 Environmental Sustainability

Requirements:

Consider environmental impact of AI systems
Optimize energy efficiency where possible
Balance performance with resource consumption
Report on environmental impact

Implementation:

Energy consumption monitoring
Green AI practices encouraged
Carbon footprint consideration in design
Annual sustainability reporting

5. GOVERNANCE STRUCTURE

5.1 AI Governance Board

Composition:

Chief Executive Officer (Chair)
Chief Technology Officer
Chief Data Officer / Chief AI Officer
Chief Risk Officer
Chief Legal Officer
Chief Information Security Officer
Chief Privacy Officer
[Business Unit Leaders]
[External Expert (optional)]

Responsibilities:

Approve AI strategy and major initiatives
Review and approve AI policy
Oversight of high-risk AI systems
Resource allocation decisions
Risk appetite and tolerance setting
Regulatory compliance oversight
Annual reporting to Board of Directors

Meeting Frequency: Quarterly minimum, ad-hoc as needed

Decision Authority:

Approval required for high-risk AI deployments
Budget approval for AI initiatives >$[amount]
Policy exceptions and waivers
Major risk acceptance decisions

5.2 AI Ethics Committee

Composition:

Representatives from diverse backgrounds
Technical AI experts
Ethicists or philosophers
Legal and compliance experts
Business stakeholders
External experts
Civil society representatives (optional)

Responsibilities:

Ethical review of AI projects
Policy guidance and recommendations
Assessment of societal impacts
Stakeholder engagement
Best practice research and recommendations

Meeting Frequency: Monthly

Scope:

Review of high-risk AI projects (mandatory)
Review of medium-risk AI projects (selective)
Ethical dilemma resolution
Policy development input

5.3 Chief AI Officer / Chief Data Officer

Responsibilities:

Overall accountability for AI policy implementation
AI strategy development and execution
Organization-wide AI coordination
Compliance monitoring
Stakeholder communication
Team leadership and capability building

Authority:

Approve AI projects within defined limits
Halt AI projects with unacceptable risks
Resource allocation within budget
Policy interpretation

Reporting: Reports to [CTO/CEO]

5.4 AI Risk Officer

Responsibilities:

AI risk identification, assessment, and management
Compliance monitoring and reporting
Incident coordination and response
Risk metrics and reporting
Control effectiveness assessment

Authority:

Require risk assessments
Halt deployments with unacceptable risks
Escalate to AI Governance Board
Request audits

Reporting: Reports to Chief Risk Officer

5.5 Data Governance Team

Composition:

Data Stewards
Data Engineers
Privacy Officers
Legal representatives
Domain experts

Responsibilities:

Data quality management
Data lineage and cataloging
Access control management
Privacy compliance
Data-related policy development

5.6 Model Validation Team

Composition:

ML Engineers (independent of development)
Domain experts
Quality assurance specialists
Fairness experts

Responsibilities:

Independent model validation
Performance testing
Fairness assessment
Robustness testing
Documentation review

Independence: Must be independent from model development teams

6. AI LIFECYCLE REQUIREMENTS

6.1 Planning and Impact Assessment

Requirements:

For all AI projects: ☐ Clear business problem and success criteria defined ☐ AI Impact Assessment completed ☐ Risk assessment conducted ☐ Stakeholder analysis performed ☐ Alternative approaches considered (including non-AI) ☐ Data requirements and availability assessed ☐ Ethical review for high-risk systems ☐ Project approval obtained

AI Impact Assessment shall include:

Purpose and intended use
Affected stakeholders
Potential benefits and harms
Fairness and bias considerations
Data requirements and privacy
Regulatory requirements
Resource requirements
Success metrics

6.2 Data Management

Requirements:

Data quality standards met
Data provenance documented
Representative and unbiased data
Privacy and security controls implemented
Data governance procedures followed
Training data versioned and traceable

Standards:

Accuracy: >[95%]
Completeness: <[5%] missing values
Timeliness: Data not older than [specify]
Representativeness: Demographic representation validated

Documentation:

Datasheet for Datasets created
Data lineage tracked
Quality metrics recorded
Known limitations documented

6.3 Development

Requirements:

Secure development environment
Version control for code, data, and models
Fairness testing during development
Explainability mechanisms implemented
Documentation standards followed
Peer review conducted

Standards:

Code review required before deployment
Model card created
Reproducibility ensured
Testing coverage >[80%]

6.4 Validation and Testing

Requirements:

Comprehensive testing performed
Performance validated across demographic groups
Fairness metrics within acceptable thresholds
Robustness testing conducted
Independent validation for high-risk systems
Acceptance criteria met

Testing Requirements:

Performance testing on held-out test set
Fairness testing: max [5%] disparity across groups
Edge case testing
Adversarial robustness testing
Integration testing

Acceptance Criteria:

Performance: [specify metrics and thresholds]
Fairness: <[5%] disparity across demographic groups
Latency: <[specify] ms for [percentile]
Error rate: <[1%]

6.5 Deployment

Requirements:

Deployment readiness assessment completed
Approval obtained from appropriate authority
Phased deployment strategy where appropriate
User training completed
Monitoring infrastructure in place
Incident response procedures ready
Transparency disclosures made

Deployment Strategies (risk-based):

High-risk: Shadow deployment followed by canary
Medium-risk: Canary deployment
Low-risk: Standard deployment with monitoring

Approval Authority:

Low-risk: Technical Lead
Medium-risk: Department Head
High-risk: AI Governance Board

6.6 Operations and Monitoring

Requirements:

Continuous performance monitoring
Data quality monitoring
Drift detection
Fairness monitoring
Incident detection and response
User feedback collection
Regular revalidation

Monitoring Frequency:

Performance: Real-time
Data quality: Real-time
Drift: Daily
Fairness: Daily for high-risk, weekly for others
Comprehensive review: [Quarterly]

Revalidation Schedule:

High-risk systems: Quarterly
Medium-risk systems: Semi-annually
Low-risk systems: Annually

6.7 Maintenance and Updates

Requirements:

Regular performance reviews
Model updates when needed
A/B testing of updated versions
Compliance with current regulations
Documentation kept current

Update Triggers:

Performance degradation >[5%]
Fairness violation detected
Drift exceeds thresholds
Regulatory changes
Critical incidents

6.8 Decommissioning

Requirements:

Planned retirement process
Data retention/deletion per policy
User communication
Knowledge preservation
Lessons learned documentation

7. RISK MANAGEMENT

7.1 Risk Assessment

Requirements:

Mandatory risk assessment for all AI systems
Risk classification: Low, Medium, High
Risk register maintained
Regular risk reassessment
Management review and approval

Risk Factors:

Impact on individuals (rights, safety, economic)
Decision consequences and reversibility
Scale of deployment
User vulnerability
Technical maturity
Data sensitivity
Regulatory applicability

7.2 Risk Classification

High-Risk AI Systems:

EU AI Act Annex III systems
Systems affecting fundamental rights
Safety-critical systems
Large-scale deployment to vulnerable populations
[Organization-specific criteria]

High-Risk Requirements:

AI Ethics Committee review (mandatory)
Independent validation
Human-in-the-loop or human-on-the-loop
Enhanced monitoring
Quarterly revalidation
Comprehensive documentation
Governance Board approval for deployment

Medium-Risk AI Systems:

Significant but not fundamental impact
Moderate scale
Reversible decisions
[Organization-specific criteria]

Medium-Risk Requirements:

Standard validation
Human-on-the-loop or appropriate oversight
Regular monitoring
Semi-annual revalidation
Standard documentation

Low-Risk AI Systems:

Minimal impact on individuals
Low scale or internal use
Easily reversible
[Organization-specific criteria]

Low-Risk Requirements:

Standard development process
Periodic audits
Annual revalidation

7.3 Risk Treatment

Controls:

Multiple layers of controls
Preventive, detective, and corrective controls
Risk-proportionate controls
Regular control effectiveness review

Risk Acceptance:

Residual risk acceptance by management
Documentation of risk acceptance rationale
Governance Board approval for high risks
Regular review of accepted risks

8. COMPLIANCE OBLIGATIONS

8.1 EU Artificial Intelligence Act

For AI systems subject to the EU AI Act:

Classification according to risk level
Conformity assessment procedures
CE marking (where required)
Registration in EU database (for high-risk)
Post-market monitoring
Incident reporting to authorities
Documentation and record-keeping

8.2 Data Protection (GDPR)

Legal basis for data processing
Data Subject Rights procedures
Privacy Impact Assessments
Data Processing Agreements with vendors
Cross-border data transfer mechanisms
Breach notification procedures

8.3 Sector-Specific Regulations

[Customize based on your industry]:

Financial services: [specify regulations]
Healthcare: [specify regulations]
Other: [specify regulations]

8.4 Standards Compliance

ISO 42001: AI Management System
ISO 27001: Information Security (where applicable)
[Industry-specific standards]

9. TRANSPARENCY AND COMMUNICATION

9.1 Internal Communication

Requirements:

Regular updates to employees
Training on responsible AI
Clear escalation paths
Incident reporting channels
Best practice sharing

Channels:

Intranet and internal communications
Training programs
Town halls and Q&A sessions
AI Community of Practice

9.2 External Communication

Requirements:

Clear disclosure of AI use to users
Transparency reports (annual)
Stakeholder engagement
Public accountability
Clear communication channels

Disclosures:

When users interact with AI
How AI influences decisions
AI capabilities and limitations
How to provide feedback
How to challenge decisions

9.3 Individual Rights

All individuals have the right to:

Be informed about AI use
Receive explanations for AI decisions
Request human review of consequential decisions
Contest or appeal AI decisions
Data subject rights (access, rectification, erasure, etc.)

Implementation:

Clear processes for exercising rights
Response within [30] days
No charge for reasonable requests
Appeals process available

10. TRAINING AND COMPETENCE

10.1 Required Training

All Staff:

Responsible AI principles
AI policy overview
How to report concerns
Frequency: Annually

AI Development Teams:

Responsible AI development
Fairness and bias mitigation
Explainability techniques
Security and privacy
Documentation requirements
Frequency: Annually + updates

AI Operators/Reviewers:

Specific AI system training
Human oversight procedures
Override and escalation
Automation bias awareness
Frequency: Before deployment + refreshers

Management:

AI governance
Risk management
Strategic implications
Regulatory requirements
Frequency: Annually

10.2 Competency Requirements

AI Roles:

Technical competency requirements
Domain expertise
Ethics awareness
Communication skills

Certification:

Training completion required
Competency assessment
Recertification annually

11. THIRD-PARTY MANAGEMENT

11.1 Vendor Assessment

Requirements:

Due diligence before engagement
Risk assessment
Compliance verification
References and track record
Financial stability

Assessment Criteria:

Technical capabilities
Security practices
Privacy compliance
Fairness and ethics practices
Incident history

11.2 Contractual Requirements

Mandatory Contract Terms:

Compliance with this AI policy
Transparency about AI systems used
Data handling requirements
Security standards
Liability and indemnification
Audit rights
Termination for cause
Incident notification

11.3 Ongoing Monitoring

Requirements:

Regular performance reviews
Compliance verification
Risk reassessment
Contract renewal criteria
Issue escalation and resolution

12. MONITORING AND REVIEW

12.1 Performance Monitoring

Metrics:

AI system performance
Fairness and bias indicators
User satisfaction
Incident rates
Compliance status

Reporting:

Monthly: Operational metrics
Quarterly: Performance reviews to Governance Board
Annually: Comprehensive report to Board of Directors

12.2 Compliance Monitoring

Activities:

Policy adherence audits
Regulatory compliance checks
Control effectiveness testing
Documentation reviews

Frequency:

Continuous automated monitoring
Quarterly compliance reviews
Annual comprehensive audits

12.3 Policy Review

Requirements:

Annual policy review (minimum)
Updates for regulatory changes
Incorporation of lessons learned
Stakeholder feedback integration
Board approval for major changes

Review Triggers:

Annual review date
Significant regulatory changes
Major incidents or findings
Technology evolution
Stakeholder feedback

13. INCIDENT MANAGEMENT

13.1 Incident Reporting

Reportable Incidents:

Performance failures
Fairness violations
Security breaches
Privacy violations
Compliance violations
Safety incidents
Reputational risks

Reporting Procedures:

Immediate reporting required
Multiple reporting channels
No-blame culture
Protection for reporters
Documentation requirements

13.2 Investigation and Response

Process:

Incident detection and reporting
Initial triage and severity assessment
Containment and immediate response
Investigation and root cause analysis
Remediation and corrective actions
Communication to stakeholders
Post-incident review
Preventive measures implementation

Response Times:

Critical (P0): <15 minutes
High (P1): <1 hour
Medium (P2): <4 hours
Low (P3): <24 hours

13.3 Learning and Improvement

Requirements:

Lessons learned documentation
Policy and procedure updates
Organization-wide communication
Training updates
Control improvements

14. VIOLATIONS AND ENFORCEMENT

14.1 Compliance Expectations

This policy is mandatory
All personnel must comply
Violations will be investigated
Consequences may be severe

14.2 Violations

Investigation Process:

Prompt investigation of alleged violations
Fair and impartial process
Documentation of findings
Management review

Consequences:

Corrective action plans
Additional training
Disciplinary action (up to termination)
Legal action (if warranted)
Reporting to regulators (if required)

14.3 Non-Retaliation

No retaliation for good-faith reporting
Protection for whistleblowers
Anonymous reporting options available

15. POLICY GOVERNANCE

15.1 Policy Owner

Chief AI Officer / Chief Data Officer:

Overall policy accountability
Policy maintenance and updates
Implementation oversight
Exception management
Reporting to governance bodies

15.2 Policy Approval

Approval Authority:

AI Governance Board: Initial approval
Board of Directors / CEO: Final approval
Required approvals for changes

15.3 Policy Distribution

Distribution:

All employees and contractors
Third-party vendors (relevant sections)
Public website (summary)
Onboarding for new employees

15.4 Exceptions

Exception Process:

Written exception request with business justification
Risk assessment of exception
Compensating controls identified
Approval by [Chief AI Officer / AI Governance Board]
Documentation and periodic review
Expiration date set

Exception Authority:

Low-risk: Chief AI Officer
Medium/High-risk: AI Governance Board

16. RELATED DOCUMENTS

AI Standard Operating Procedures
AI Risk Assessment Methodology
Model Development Guidelines
Model Validation Framework
AI Incident Response Plan
Data Governance Framework
Privacy Impact Assessment Template
Model Card Template
AI System Inventory

17. DEFINITIONS

Artificial Intelligence (AI): [Definition per EU AI Act or ISO 42001]

Machine Learning: [Definition]

High-Risk AI System: [Definition]

Bias: [Definition]

Fairness: [Definition]

Explainability: [Definition]

Additional terms as needed

18. DOCUMENT CONTROL

Version	Date	Changes	Author	Approved By
1.0	[Date]	Initial version	[Name]	[Name]

APPROVAL

This Artificial Intelligence Policy has been reviewed and approved by:

AI Governance Board:

[Chair Name], Chair Date: __________

Chief Executive Officer:

[CEO Name], Chief Executive Officer Date: __________

Board of Directors (if applicable):

[Board Chair Name], Board Chair Date: __________

Implementation Guidance

After Policy Approval

Communication Plan:
- Executive announcement
- All-hands presentation
- Department-specific briefings
- FAQ document
- Q&A sessions
Training Rollout:
- Schedule training sessions
- Develop training materials
- Track completion
- Assess comprehension
Process Implementation:
- Create detailed procedures
- Develop templates and tools
- Assign responsibilities
- Set up monitoring
Technology Enablement:
- Deploy required tools
- Configure monitoring systems
- Set up dashboards
- Implement access controls
Compliance Monitoring:
- Establish metrics
- Create dashboards
- Schedule audits
- Report to governance

First 90 Days

Week 1-2: Communication and awareness
Week 3-4: Training begins
Week 5-6: Procedures and tools deployed
Week 7-8: AI system inventory completed
Week 9-10: Risk assessments for existing AI
Week 11-12: First governance committee meeting

Ongoing

Monthly: Metrics review
Quarterly: Governance committee meetings
Semi-annually: Compliance audits
Annually: Policy review and update

This policy template provides a comprehensive foundation. Customize it to your organization's specific needs, risk profile, and regulatory environment.

Next Lesson: Control Implementation Checklist - Comprehensive checklist for implementing all ISO 42001 controls with practical guidance.