Artificial intelligence is increasingly used in business operations, healthcare, transportation, finance, decision-making, and other critical environments. As AI adoption grows, organizations must demonstrate that their systems are governed responsibly, risks are controlled, and relevant legal, ethical, and organizational requirements are addressed.
This course provides a practical, step-by-step guide to auditing an Artificial Intelligence Management System in accordance with ISO/IEC 42001. It explains the complete audit lifecycle, from understanding the standard and preparing the audit to reporting findings, closing the audit, and monitoring corrective actions.
You will begin with an introduction to Artificial Intelligence Management Systems and the structure of ISO/IEC 42001. The course examines Clauses 4 through 10, including organizational context, leadership, planning, support, operations, performance evaluation, and continual improvement.
Using NeuroVista Technologies as the main case study, you will see how ISO 42001 requirements can be assessed within a realistic organizational environment. A second fictional organization, AutoVision AI, is used for assignments and practical audit exercises.
The course then introduces the core principles of auditing, including integrity, objectivity, confidentiality, evidence-based decision-making, and professional judgement. You will learn how to apply a risk-based approach when evaluating AI systems and prioritize areas involving significant ethical, security, privacy, operational, and compliance risks.
Special attention is given to AI-specific audit concerns such as bias, fairness, transparency, explainability, accountability, model lifecycle governance, data protection, human oversight, and security controls.
You will learn how to define audit objectives and scope, identify relevant AI governance controls, prepare checklists and questionnaires, allocate resources, create an audit schedule, and conduct an effective opening meeting.
During the audit-execution stages, you will learn how to review AI policies, risk assessments, governance records, technical documentation, and lifecycle evidence. You will also explore stakeholder interviewing techniques and methods for collecting reliable objective evidence.
The course explains how to assess AI governance and security controls, evaluate transparency and explainability practices, and determine whether bias and fairness risks are being managed effectively.
You will then learn how to identify and classify major and minor nonconformities, write clear audit findings, recommend corrective actions, prepare professional audit reports, and communicate results to relevant stakeholders.
Finally, the course covers closing meetings, corrective-action implementation, follow-up activities, and continual improvement in AI governance. Practical checklists, interview guides, risk-assessment forms, audit templates, and reporting formats support the learning process.
