ISO 42001 AI Policy

Overview

Draft and validate the formal AI Policy for ISO/IEC 42001:2023, approved by top management.

Getting Started

Add your source materials to the project workspace, then activate the ISO 42001 AI Policy skill. The agent will guide you through the process step by step.

Example conversation

You: We need a formal AI policy for top management approval that covers risk, transparency, data governance, and ethics for our ISO 42001 certification.

Agent: I’ll draft the AI policy using the skill’s requirements: management of AI risks and sensitive data; transparency, oversight, and explainability; data governance expectations; ethical and responsible AI use; alignment with organizational purpose and continual improvement. I’ll keep it concise and then run ai_policy_check to ensure all required elements are present before you submit for approval.

Output excerpt

AI policy excerpt

Risk and sensitive data — The organization manages AI-related risks through a defined risk assessment methodology and risk register. Sensitive data used in or affected by AI systems is governed by our data governance procedures and data protection policy.

Transparency and explainability — We are committed to transparency in the use of AI: users and affected parties are informed where AI supports or makes decisions. We maintain explainability appropriate to the risk level of each system and document limitations in system descriptions.

Data governance — Data used for training and operation of AI systems is subject to quality, provenance, and security requirements as set out in our data governance procedures. We assess data bias and representativeness where relevant.

Ethical and responsible use — AI is used in line with our values and legal obligations. We conduct impact assessments for significant AI systems and ensure human oversight and accountability.

Extension and validation

The skill includes ai_policy_check, which validates the AI policy draft for required elements: risk and sensitive data management; transparency, oversight, and explainability; data governance expectations; ethical and responsible AI use. Run it after drafting to confirm nothing is missing before submission or audit.