List of Mandatory ISO 42001 Documented Procedures

ISO 42001 is the international standard for Artificial Intelligence Management Systems (AIMS). It requires organizations to define and document key procedures that ensure responsible development, deployment, and operation of AI systems. These mandatory procedures provide a structured framework for governance, risk management, data quality, and ongoing monitoring. Each procedure is formally controlled and regularly updated to maintain compliance and drive continuous improvement in AI operations. Below is an overview of ISO 42001 documented procedures required for compliance.

Management Review Procedure

Management review ensures top management regularly evaluates the AI system’s performance and objectives. It covers AI policies, controls, and risks, with outcomes documented to guide improvement.

Change Management

Change management controls changes to the AI system or management processes. Proposed changes (e.g., models, algorithms, data, or infrastructure) are reviewed and approved, ensuring impacts on risk, compliance, and objectives are assessed before implementation.

Documented Information Control

Documented information control specifies how AI-related documents and records are created, updated, reviewed, distributed, and archived. It ensures policies, procedures, and data are accurate, version-controlled, and accessible only to authorized personnel under defined retention requirements.

Procedure for AI System Impact Assessment

AI system impact assessment outlines how to evaluate an AI system’s social, ethical, legal, and operational impacts. It defines assessment triggers, assigns responsible parties, and integrates findings (e.g., fairness or bias issues) into AI design and operation.

Corrective Action

Corrective action defines how nonconformities or failures in the AI management system are identified, investigated, and remedied. It includes determining root causes, implementing fixes, and documenting effectiveness of actions taken.

Data Management

Data management governs how data for AI systems is collected, stored, processed, and protected. It ensures data is handled securely and in compliance with privacy, ethical, and quality requirements, including inventory, access controls, and disposal.

Control of Records

Control of records ensures that all AI management system records (e.g., audit reports, impact assessments, training logs) are properly maintained. It defines how records are stored, protected, and retained for a specified period as evidence of compliance.

Data Quality

Data quality establishes processes to ensure data used in AI development and operation is accurate, complete, and fit for purpose. It includes validation checks, cleansing routines, and monitoring to prevent errors or biases undermining AI outcomes.

AI Management System Internal Audit

AI internal audit defines how to perform internal audits of the AI management system, including planning, defining ISO 42001-based criteria, evaluating processes, reporting nonconformities, and following up on corrective actions to ensure effectiveness.

Scope Documentation for Implementation

Scope documentation specifies the AI management system’s boundaries, detailing which parts of the organization, AI technologies, processes, and locations are covered by ISO 42001. This prevents ambiguity and ensures consistent application of procedures.

Risk Management Procedure

Risk management outlines how to identify, assess, and treat AI-related risks (security, privacy, ethical, operational). It includes maintaining a risk register, evaluating likelihood and impact, and implementing mitigation measures. Regular reviews address emerging threats.

AI Life Cycle Development

AI life cycle development governs the entire AI system lifecycle from concept through deployment and retirement. It defines stages, responsibilities, and quality checks at each phase to ensure systems are built and maintained per best practices.

Human Resources and Training Procedure

Human resources and training ensure personnel involved in AI activities have necessary qualifications. It covers role definitions, competencies, training programs (ethics, security, compliance), and it documents training records.

Monitoring and Measurement of Processes

Monitoring and measurement specifies how the organization will track and analyze AI management processes and system performance. It defines KPIs, measurement methods, and reporting intervals. Results assess effectiveness and drive continual improvement.

Customer Relationship

Customer relationship governs how the organization interacts with customers regarding AI products and services. It includes collecting feedback, handling inquiries or complaints about AI behavior, and communicating relevant AI policies or changes.

Managing Security Threats & Vulnerabilities

Managing security threats & vulnerabilities focuses on identifying security risks specific to AI systems. It includes regular assessments, vulnerability scanning of AI models and infrastructure, and applying defenses against attacks to protect assets and data.

AI Incident Management

AI incident management defines how to respond when AI-related incidents occur (such as system failures, data breaches, or ethical issues). It outlines detection, reporting, investigation, corrective actions, and communication plans to restore operations quickly.

Supplier and Contractor Management

Supplier and contractor management ensures third-party vendors and contractors involved in AI meet the organization’s AI governance standards. It covers supplier selection, security and ethics requirements in contracts, and performance monitoring for consistent compliance.

For ISO 42001:2023 Implementation check complete ISO 42001 documents – which include ISO 42001 manual, standard operating procedures (sop’s), documentation checklist, forms, flowcharts, job descriptions, etc.