Lead Auditor Training
ISO 42001
The first standard for AI Management Systems
Duration:
12 Hours (4h Prework + 8h Live Training)
Format:
Live Online, Instructor-Led
Language:
English & Spanish
Benefits of Getting Certified in ISO/IEC 42001
Greater Professional Recognition
ISO 42001 certification highlights the auditor as a specialist in AI auditing, enhancing their credibility and prestige within the industry
Increased Business Value
AI audits strengthen organizational
trust and reputation, fostering long-
lasting relationships with clients
Expansion of Opportunities
The growing demand for AI audits
creates opportunities for employment
and specialization in a key and rapidly
expanding field
What you will learn in this course
1. Overview of ISO/IEC 42001
Understand its purpose, scope, structure, and relation to other ISO standards.
2. Key AI Management System Requirements
Identify governance, risk management, ethics, and compliance requirements for AIMS.
3. Auditing AI Management Systems
Apply audit principles to assess AI-related risks, compliance, and ethical practices.
4. AI Governance & Risk Management
Evaluate AI risks like bias, transparency, accountability, and regulatory alignment.
5. Ethical & Legal AI Considerations
Ensure AI fairness, accountability, compliance, and ethical risk management.
6. Conducting AI Management Audits
Plan, execute, and report AI system audits under ISO/IEC 42001 standards.
Review audit findings, report on nonconformities, and evaluate corrective actions specific to AI.
What Our Auditors Say About Our Training
Curriculum
Module 1: Introduction to ISO/IEC 42001:2023
- Overview of AI and its role in modern organizations
- Introduction to ISO/IEC 42001:2023 and its structure
- Integration with other ISO management system standards (e.g., ISO 9001, ISO/IEC 27001)
- Scope and objectives of the AI management system (AIMS) standard
Module 2: Key Requirements of the AI Management System
- Governance structure for AI systems
- Ethical and legal compliance
- Risk management for AI, including bias, transparency, and accountability
- Data management and protection in AI systems
- Continual improvement and AI performance evaluation
Module 3: AI Risk and Ethics in Practice
- Identifying AI-related risks (e.g., bias, security vulnerabilities, lack of transparency)
- Addressing ethical considerations (e.g., fairness, accountability, transparency)
- Legal and regulatory landscape for AI systems
- Integrating ethical AI practices into management systems
- AI controls – Statement of applicability
