ISO 42001:2023 Control 2.2 AI policy

Alignment with Business Strategy and Risk Tolerance

Your AI policy must align with organizational objectives, culture, and risk management practices. The policy should define:

• How AI supports business goals such as efficiency, automation, customer experience, or innovation.
• What level of risk is acceptable for AI-related operations and decision-making.
• How AI will be integrated into existing business processes, minimizing operational disruptions.
• Guidelines for evaluating AI performance in relation to business objectives.

Organizations must establish a risk appetite for AI, ensuring that AI-driven decisions align with strategic goals.

Legal, Regulatory, and Compliance Considerations

AI policies must comply with applicable legal, regulatory, and contractual requirements to prevent legal liabilities. The policy should address:

• Data privacy laws (e.g., GDPR, CCPA) – AI models must comply with regulations governing personal data processing.
• AI-specific regulations – Some jurisdictions have AI governance laws that mandate transparency, accountability, and bias mitigation.
• Intellectual property laws – AI development should adhere to licensing, copyrights, and IP protection.
• Contractual obligations – AI-related vendor and third-party agreements must be reviewed for compliance.

Failure to integrate these legal aspects can result in regulatory fines, data protection violations, and loss of stakeholder trust.

AI Risk Management and Security Controls

To mitigate AI-related risks, the policy should include risk assessment and mitigation strategies:

• AI Impact Assessments (AIIA) – Evaluating AI’s potential risks before deployment.
• Bias and Fairness Testing – Preventing discrimination in AI-driven decisions.
• Cybersecurity Controls – Securing AI systems against adversarial attacks and data breaches.
• Data Governance – Establishing rules for AI training data collection, storage, and processing.
• Incident Response Plan – Defining how to handle AI-related security incidents and ethical concerns.

AI risk management is an ongoing process requiring continuous monitoring, auditing, and refinement.

Principles Governing AI Development and Use

Your AI policy should define core principles to guide AI-related activities. These principles include:

• Transparency – Ensuring AI decision-making processes are explainable and auditable.
• Fairness – Mitigating biases and ensuring equal treatment across all AI-driven processes.
• Security – Implementing robust cybersecurity measures to protect AI systems.
• Accountability – Assigning responsibility for AI governance and compliance.
• Sustainability – Ensuring AI operations align with ethical and environmental standards.

Clearly defined principles enhance AI trustworthiness and regulatory compliance.

AI System Lifecycle Management

AI systems undergo several stages, from development to deployment and eventual retirement. The AI policy must establish governance across the AI system lifecycle:

• Development Phase – Ensuring AI models are built securely and ethically.
• Testing and Validation – Conducting fairness and security tests before deployment.
• Deployment Guidelines – Implementing AI models in production environments securely.
• Monitoring and Auditing – Continuously evaluating AI performance and compliance.
• Retirement and Decommissioning – Defining policies for safely phasing out AI systems.

This structured approach ensures AI systems remain secure, compliant, and effective throughout their lifecycle.

Handling Policy Deviations and Exceptions

AI policies may require exceptions in specific scenarios. Organizations must define:

• Who can approve deviations from the AI policy.
• How exceptions are documented and reviewed.
• The process for updating policies to reflect new risks or regulatory changes.