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Section 1: Foundations of AIMS Auditing

Module 1: What an AIMS internal audit is

Module 2: Auditing the AIMS using ISO 19011 logic

Section 2: Auditing AI Governance and Risk Management

5. Leadership

8.3 AI risk assessment

Section 3: Auditing AI Resources and Lifecycle Controls

7. Support

Example Audit Trail

Section 4: External Dependencies, Incidents, and Reporting

8.5 Third-party and externally provided processes, products or services

8.6 Incident management and response

Module 3: Evidence, questions and reporting in practice

Section 5: Knowledge Checks

Quiz 1: AIMS Auditing Fundamentals

Quiz 2: Auditing AI Leadership and Planning

Quiz 3: AI Support and Competence Evidence

Quiz 4: Lifecycle Audit Trails and Annex A Controls

Quiz 5: Third-Party AI, Incidents, and Audit Reporting

Section 6: Course Feedback

Post-Course Survey: Internal Auditing for AIMS

As Artificial Intelligence (AI) continues to reshape industries, organizations must ensure their AI systems are developed, deployed, and managed responsibly. The ISO/IEC 42001 standard provides the world's first comprehensive framework for an Artificial Intelligence Management System (AIMS).

As Artificial Intelligence (AI) continues to reshape industries, organizations must ensure their AI systems are developed, deployed, and managed responsibly. The ISO/IEC 42001 standard provides the world's first comprehensive framework for an Artificial Intelligence Management System (AIMS). However, implementing the standard is only half the battle; organizations need skilled internal auditors to verify compliance, manage risks, and drive continual improvement. Welcome to 'Internal Auditing for ISO/IEC 42001 AI Management Systems,' a comprehensive course designed to equip you with the knowledge and practical skills to audit AI governance effectively.

Whether you are transitioning from ISO 9001 or ISO/IEC 27001, or you are an AI professional stepping into compliance, this course bridges the gap between traditional auditing principles (ISO 19011) and cutting-edge AI risk management. You will explore the foundations of AIMS auditing, learning how to evaluate leadership commitment, robust AI risk assessments, and lifecycle controls. Through practical examples and audit trails, you will discover how to assess third-party AI dependencies, incident response protocols, and resource allocation without needing to be a technical AI developer.

By the end of this course, you will be able to formulate clear, actionable audit findings and write reports that provide immense value to top management. Step into the future of compliance and become a vital asset to your organization's responsible AI journey. Enroll today to master the internal auditing processes for ISO/IEC 42001 and ensure your organization's AI systems are safe, transparent, and compliant.

ISO/IEC 42001 - Internal Auditing of AI Management Systems for Internal Auditors

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            <p>Artificial intelligence is rapidly transforming how organizations operate, bringing incredible opportunities alongside entirely new categories of risk. As an experienced internal auditor, quality professional, or compliance lead, you already know the mechanics of auditing management systems like ISO 9001 or 27001. Now, with the introduction of ISO/IEC 42001, the challenge is adapting those proven audit principles to the unique complexities of an Artificial Intelligence Management System (AIMS). This course bridges that gap, helping you translate your existing knowledge of ISO 19011 logic into the specific context of AI governance, risk management, and lifecycle controls, all without requiring you to become a technical AI software engineer.</p>

<p>Throughout this focused orientation, we will explore exactly what "good" looks like when assessing an organization's AI framework. We will start with the foundations of AIMS auditing, clarifying key terminology and showing you how to plan an audit tailored to specific AI use cases. From there, we will dive into auditing leadership commitment and robust AI risk assessments, ensuring that top management is truly driving safe AI implementation. You will also learn how to trace a practical audit trail right through the AI system lifecycle, evaluate third-party AI dependencies, and review incident management procedures. By the end of our time together, you will be equipped with the right questions, evidence sources, and reporting strategies to confidently incorporate AI into your existing internal audit programs.</p>
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