Lead AI Risk Manager

Mastering AI Risk Management for Responsible and Compliant Innovation

The Lead AI Risk Manager training course provides participants with the critical knowledge and practical skills to effectively identify, assess, mitigate, and manage risks associated with artificial intelligence systems. Drawing upon globally recognized frameworks such as the NIST AI Risk Management Framework, the EU AI Act, and real-world case insights from the MIT AI Risk Repository, this course offers a structured approach to AI governance, regulatory alignment, and ethical risk oversight.

Participants will work through real-life scenarios and case studies from the MIT repository to strengthen their ability to navigate emerging AI risk challenges and apply effective mitigation strategies.

Why Should You Attend?

Artificial Intelligence is revolutionizing industries, accelerating productivity and innovation—but also introducing complex, unprecedented risks. From algorithmic bias and data privacy concerns to security vulnerabilities and lack of transparency, the effective management of AI-related risks has become a strategic imperative.

This course prepares professionals to implement responsible AI governance practices and compliance frameworks. Upon successful completion of the exam, participants can apply for the internationally recognized PECB Certified Lead AI Risk Manager credential, demonstrating their expertise in managing AI-related risks across organizational contexts.

Who Should Attend?

This training course is designed for:

• Professionals responsible for AI risk governance and compliance

• IT and security experts involved in deploying or securing AI systems

• Data scientists, ML engineers, and AI developers

• Risk managers and internal auditors addressing AI-related vulnerabilities

• Legal, compliance, and ethics officers focused on AI regulation and societal impact

• Consultants advising organizations on safe and responsible AI use

• Executives and decision-makers leading AI transformation initiatives

Learning Objectives

By the end of this course, participants will be able to:

• Understand the foundations and methodologies of AI risk management

• Identify and assess risks such as bias, opacity, misuse, and cybersecurity threats

• Develop and implement mitigation and incident response strategies

• Apply frameworks such as the NIST AI RMF and EU AI Act to ensure AI accountability

• Align AI projects with organizational risk tolerance, compliance, and ethical standards

Educational Approach

• Combines foundational theory with real-world case applications

• Includes scenario-based exercises, role plays, and interactive discussions

• Features quizzes modeled after the certification exam for exam readiness

• Encourages collaboration and peer exchange for enhanced learning impact

Prerequisites

Participants should have a general understanding of artificial intelligence and core risk management concepts. Familiarity with AI governance frameworks such as the NIST AI RMF or the EU AI Act is recommended but not required.

• Certificate and examination fees are included in the price of the training course.
• Candidates who have completed the training course but failed the exam are eligible to retake the exam once for free within a 12-month period from the initial date of the exam.