Iso 42001 Annex A Controls Explained

Master ISO/IEC 42001 Annex A controls with practical examples, governance checklists, audits, and responsible AI

What you'll learn

Understand each ISO 42001 Annex A control and its purpose

Identify real-world risks and governance requirements in AI systems

Map ISO 42001 controls to AI lifecycle and compliance efforts

Apply Annex A controls using checklists, templates, and case studies

Requirements

No prior knowledge of ISO 42001 is required. A basic understanding of AI, compliance, or risk management is helpful. The course is self-contained, and all templates, walkthroughs, and examples are provided for practical understanding.

Description

This course contains the use of artificial intelligence. Led by Dr. Amar Massoud, a seasoned expert with decades of academic and professional experience, it combines cutting-edge AI support with human insight to deliver content that is precise, practical, and easy to follow. You’ll gain the clarity of structured learning and the confidence of being guided by a recognized authority.ISO/IEC 42001:2023 is the world’s first international standard for AI Management Systems (AIMS), providing a robust framework for governing AI systems ethically, securely, and transparently. In this course, we provide a comprehensive walkthrough of all Annex A controls, ensuring you understand their intent, scope, and real-world application.Whether you're preparing for an audit, building AI compliance programs, or enhancing organizational accountability, this course will equip you with practical knowledge and tools. Each control is explained with slide notes, control checklists, InfoSure Ltd. use-case examples, and audit techniques. You’ll explore how to implement, assess, and document conformance for topics such as fairness, bias mitigation, AI lifecycle governance, data quality, stakeholder impact, transparency, supplier obligations, and responsible use.What You’ll Learn:Understand the purpose and scope of each Annex A control in ISO/IEC 42001Apply controls across the AI lifecycle—from data acquisition to model deployment and monitoringConduct gap assessments and audits using structured templates and checklistsMap Annex A requirements to real-world AI systems using model company examplesKey Features:Fully aligned with ISO/IEC 42001:2023Practical examples using the fictional company InfoSure Ltd.Downloadable templates for audit readiness and implementationAI governance concepts tailored for auditors, developers, and compliance professionalsThis course is ideal for professionals working in AI governance, compliance, auditing, risk management, AI ethics, and quality assurance. It’s also a powerful resource for AI developers and solution architects seeking to align their systems with international best practices.By the end of this course, you’ll not only understand what each control requires—but also how to apply, verify, and continuously improve them in real-world AI contexts. Join now and take a critical step toward mastering responsible, auditable AI governance.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Model Company – Synthovia HealthTech Ltd

Section 2: AI Policies and Governance Structures

Lecture 3 AI Policies and Governance Structures

Lecture 4 Control A.2.2 – AI Policy

Lecture 5 Control A.2.3 – Alignment with Other Organizational Policies

Lecture 6 Control A.2.4 – Review of the AI Policy

Lecture 7 Control A.3.2 – AI Roles and Responsibilities

Lecture 8 Control A.3.3 – Reporting of Concerns

Section 3: AI System Resources and Inventory

Lecture 9 AI System Resources and Inventory

Lecture 10 Control A.4.2 – Resource Documentation

Lecture 11 Control A.4.3 – Data Resources

Lecture 12 Control A.4.4 – Tooling Resources

Lecture 13 A.4.5: System and Computing Resources

Lecture 14 Control A.4.6 – Human Resources

Section 4: Assessing the Impact of AI Systems

Lecture 15 Assessing the Impact of AI Systems

Lecture 16 Control A.5.2 – AI System Impact Assessment Process

Lecture 17 Control A.5.3 – Documentation of AI System Impact Assessments

Lecture 18 Control A.5.4 – Assessing AI System Impact on Individuals or Groups

Lecture 19 Control A.5.5 – Assessing Societal Impacts of AI Systems

Section 5: AI System Life Cycle Management

Lecture 20 AI System Life Cycle Management

Lecture 21 Control A.6.1.2 – Objectives for Responsible Development

Lecture 22 A.6.1.3 – Processes for Responsible Design and Development

Lecture 23 Control A.6.2.2 – AI System Requirements and Specification

Lecture 24 Control A.6.2.3 – Documentation of AI System Design and Development

Lecture 25 Control A.6.2.4 – AI System Verification and Validation

Lecture 26 Control A.6.2.5 – AI System Deployment

Lecture 27 Control A.6.2.6 – AI System Operation and Monitoring

Lecture 28 Control A.6.2.7 – AI System Technical Documentation

Lecture 29 Control A.6.2.8 – AI System Recording of Event Logs

Section 6: Data Governance for AI Systems

Lecture 30 Data Governance for AI Systems

Lecture 31 Control A.7.2 – Data for Development and Enhancement of AI System

Lecture 32 Control A.7.3 – Acquisition of Data

Lecture 33 Control A.7.4 – Quality of Data for AI Systems

Lecture 34 Control A.7.5 – Data Provenance

Lecture 35 Control A.7.6 – Data Preparation

Section 7: Transparency and Communication with Stakeholders

Lecture 36 Transparency and Communication with Stakeholders

Lecture 37 Control A.8.2 – System Documentation and Information for Users

Lecture 38 Control A.8.3 – External Reporting

Lecture 39 Control A.8.4 – Communication of Incidents

Lecture 40 Control A.8.5 – Information for Interested Parties

Section 8: Responsible Use of AI Systems

Lecture 41 Responsible Use of AI Systems

Lecture 42 Control A.9.2 – Processes for Responsible Use of AI Systems

Lecture 43 Control A.9.3 – Objectives for Responsible Use

Lecture 44 Control A.9.4 – Intended Use of the AI System

Section 9: Managing Third-Party and Customer Relationships

Lecture 45 Managing Third-Party and Customer Relationships

Lecture 46 Control A.10.2 – Allocating Responsibilities

Lecture 47 Control A.10.3 – Suppliers

Lecture 48 Control A.10.4 – Customers

Section 10: Conclusion

Lecture 49 Conclusion

This course is designed for AI governance professionals, compliance officers, lead auditors, data scientists, and project managers who are responsible for implementing or auditing ISO/IEC 42001:2023. It is also ideal for anyone interested in understanding how Annex A controls address transparency, fairness, bias, robustness, data management, and stakeholder responsibility in the context of AI systems.