ISO/IEC 42001:2023 – Artificial Intelligence Management System (AIMS)

Master ISO/IEC 42001:2023 for AI responsible management, ensuring ethical and compliant AI deployment.
Duration: 1 Day
Hours: 4 Hours
Training Level: All Levels
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Batch One
Thursday, June 26, 2025
12:00 PM - 04:00 PM (Eastern Time)
Batch Two
Thursday, July 31, 2025
12:00 PM - 04:00 PM (Eastern Time)
Batch Three
Wednesday, August 27, 2025
12:00 PM - 04:00 PM (Eastern Time)
Batch Four
Thursday, September 18, 2025
12:00 PM - 04:00 PM (Eastern Time)
Live Session
Single Attendee
$149.00 $249.00
Live Session
Recorded
Single Attendee
$199.00 $332.00
6 month Access for Recorded
Live+Recorded
Single Attendee
$249.00 $416.00
6 month Access for Recorded
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About the Course:

ISO/IEC 42001:2023 provides a framework for managing AI systems, ensuring ethical, transparent, and compliant deployment. This course explores the standard’s requirements, focusing on risk management, governance, and accountability in AI applications. Participants will learn to implement AIMS effectively.

The course covers practical topics like AI risk assessment, stakeholder engagement, and compliance with regulatory frameworks. Through case studies, learners will gain insights into applying ISO/IEC 42001:2023 to diverse industries, fostering trust in AI systems.

Course Objectives:

  • Understand ISO/IEC 42001:2023 requirements.
  • Learn AI risk management strategies.
  • Explore AI governance frameworks.
  • Ensure ethical AI deployment.
  • Develop AIMS implementation plans.
  • Analyze real-world AIMS applications.
  • Foster trust in AI systems.

Who is the Target Audience?

  • Engineers who do or will use AI
  • AI developers
  • Compliance officers
  • Risk management professionals
  • IT managers
  • Data scientists

Basic Knowledge:

  • Familiarity with AI technologies
  • Basic knowledge of ISO management systems
  • Understanding of regulatory compliance

Curriculum
Total Duration: 4 Hours
Introduction to ISO/IEC 42001:2023
AI Risk Assessment Frameworks
Governance for AI Systems
Ethical AI Deployment Principles
Stakeholder Engagement Strategies
Data Privacy in AI Management
Case Study: Aims in Healthcare AI
Case Study: Aims in Financial AI
Regulatory Compliance With Aims
Employee Training for Aims
Vendor Selection for AI Systems
Cost-Benefit of Aims Implementation
Scalability of AI Management Systems
Measuring Aims Effectiveness
Collaboration With Compliance Teams
Overcoming Aims Challenges
Future Trends in AI Management
Best Practices for Aims Deployment
Integration With Existing Systems
Ensuring Transparency in AI