Course Overview:
The course provides an implementation and managing ISO/IEC 42001:2023 standards for AI Management Systems (AIMS). Designed for organizations seeking to integrate AI governance and compliance, this course covers AI lifecycle management, offering actionable insights into ISO AI standards implementation, AI risk management frameworks, and AI policy development. Participants will explore key areas such as AI data governance, ethics and accountability, and AI principles for organizations. The course emphasizes the scope of AI Management Systems, detailing governance key concepts, stakeholder analysis, and the importance of aligning with ISO 27001 and other global standards. By attending, participants will gain practical expertise in AI system design best practices, continuous learning in AI, and performance monitoring to ensure a seamless and compliant implementation of AIMS.
Target Audience:
- CIOs, CTOs, and IT Managers looking to integrate AI Management Systems (AIMS)
- Compliance officers managing AI governance and compliance regulations
- Risk managers responsible for AI risk assessment techniques and mitigating AI risks
- AI developers and data scientists engaged in AI lifecycle management and AI system design best practices
- Project managers working on aligning ISO/IEC 42001 standards with organizational goals
Targeted Organizational Departments:
- Information Technology (IT): For implementing AI lifecycle management and AI performance monitoring
- Compliance and Risk Management: To ensure adherence to AI governance and compliance requirements
- Research and Development (R&D): For AI system design best practices and continuous learning in AI systems
- Data Governance Teams: To address data quality and AI data governance
Targeted Industries:
- Finance and Banking
- Healthcare
- Manufacturing
- Technology and Software Development
- Retail and E-commerce
Course Offerings:
By the end of this course, participants will be able to:
- Implement ISO/IEC 42001:2023 standards in their organization
- Develop and manage AI governance and compliance policies
- Design and monitor robust AI Management Systems (AIMS)
- Conduct comprehensive AI risk assessments and mitigate potential risks
- Align AI lifecycle management with global standards such as ISO 27001
- Address data quality, bias, and ethical challenges in AI data governance
Training Methodology:
This course utilizes a hands-on approach with interactive sessions, real-world AI management case studies, and group discussions. Participants will conduct mock AI impact assessments, analyze system failures, and work on AI compliance exercises. The program focuses on practical application through AI risk management framework development, case studies on ethics, and stakeholder analysis workshops.
Course Toolbox:
- Comprehensive course ebook on ISO/IEC 42001:2023 standards
- Checklists for AI risk assessment techniques
- Templates for AI policy development and AI governance frameworks
- Case studies illustrating lessons from AI implementation
Course Agenda:
Day 1: Introduction to ISO AI Standards
- Topic 1: Overview of ISO/IEC 42001:2023 – Scope, purpose, and benefits of AI Management Systems (AIMS)
- Topic 2: Key terminology and concepts related to AI governance and management
- Topic 3: Context of the organization – Identifying internal and external factors influencing AI systems
- Topic 4: Stakeholder analysis and their expectations in AI implementation
- Topic 5: Alignment with other standards – ISO 27001, ISO 9001, and the EU AI Act
- Topic 6: Importance of aligning AI governance with organizational goals
- Reflection & Review: Key takeaways from the foundational understanding of ISO/IEC 42001:2023
Day 2: AI Governance and Leadership
- Topic 1: Leadership and commitment – Role of leadership in implementing AI Management Systems (AIMS)
- Topic 2: Defining AI principles – Ethics, fairness, transparency, and accountability
- Topic 3: Policy development – Establishing and communicating AI management policies
- Topic 4: Roles and responsibilities – Establishing accountability frameworks for AI governance
- Topic 5: Practical approaches to integrating organizational vision into AI strategies
- Topic 6: Challenges and solutions in fostering leadership commitment for AI implementation
- Reflection & Review: Recap on AI governance strategies and leadership impact
Day 3: Risk and Impact Management in AI
- Topic 1: Introduction to AI risk management frameworks – Identifying, analyzing, and mitigating risks
- Topic 2: Risk assessment tools and techniques tailored for AI systems
- Topic 3: AI impact assessment – Evaluating social, economic, and environmental implications
- Topic 4: Case studies on effective AI risk and impact assessments
- Topic 5: Compliance and legal requirements – Aligning global AI regulations with AIMS
- Topic 6: Developing strategies to address unexpected risks in AI systems
- Reflection & Review: Applying risk and impact management insights
Day 4: AI Lifecycle and System Management
- Topic 1: AI system design and development – Managing requirements, testing, and validation
- Topic 2: Incorporating continuous learning mechanisms in AI systems
- Topic 3: Operations and monitoring – Key performance indicators for AI systems
- Topic 4: Techniques for identifying and addressing AI system failures
- Topic 5: Data governance in AI – Ensuring data quality, security, and privacy
- Topic 6: Addressing biases in AI models – Building fairness and transparency into AI governance
- Reflection & Review: Lessons learned on lifecycle management and operational improvements
Day 5: Continuous Improvement, Supplier Management, and Practical Implementation
- Topic 1: Performance evaluation – Setting measurable objectives for AIMS effectiveness
- Topic 2: Nonconformity and corrective actions – Addressing deviations and improving processes
- Topic 3: Supplier management and third-party alignment – Ensuring adherence to AI principles
- Topic 4: Real-world case studies of AIMS implementation – Success stories and lessons learned
- Topic 5: Preparing for certification – Steps to achieve compliance with ISO/IEC 42001:2023
- Topic 6: Certification process – Required documentation and strategies for continuous improvement
- Reflection & Review: Consolidating certification readiness and key takeaways from the course
How This Course is Different from Other ISO AI Standards Courses:
This course distinguishes itself by focusing on actionable insights and practical applications of ISO/IEC 42001:2023 standards. Unlike other programs, it integrates hands-on exercises such as mock AI impact assessments, comprehensive workshops on AI lifecycle management, and The application of methods for governing and ensuring compliance with artificial intelligence. By leveraging real-world AI management case studies, participants will learn how to overcome industry-specific challenges and implement AI principles for organizations effectively.
