Achetez cette formation en Self Paced (à votre rythme) et recevez les cours, les quizz, les exercices et le code pour passer l’examen dans une période de 1 an
L’option Self Paced vous donne :
- Le support de cours self paced online dans l’application web ou desktop KATE avec les exercices et les quizz et leurs corrigés
- Le voucher de l’examen avec un retake : vous aurez un an pour passer l’examen dès la génération du voucher
- La certification PECB : vous recevrez votre certification dès que vous aurez validé votre examen et rempli les conditions nécessaires
Lead AI Risk Manager
The Lead AI Risk Manager training course equips participants with the essential knowledge and skills to identify, assess, mitigate, and manage AI-related risks. Based on leading frameworks such as the NIST AI Risk Management Framework, the EU AI Act, and insights from the MIT AI Risk Repository, this course provides a structured approach to AI risk governance, regulatory compliance, and ethical risk management.
Participants will also analyze real-world AI risk scenarios from the MIT AI Risk Repository, gaining practical insights into AI risk challenges and effective mitigation strategies.
Why Should You Attend?
Artificial intelligence (AI) is transforming industries at an accelerated pace, driving innovation while simultaneously introducing new risks and challenges. As AI technologies advance, organizations must address critical concerns such as bias, security threats, transparency, and regulatory compliance. Managing these risks effectively requires a specialized approach that goes beyond traditional risk management frameworks.
The PECB Certified Lead AI Risk Manager certification validates your expertise in identifying, assessing, and mitigating AI-related risks while ensuring compliance with industry frameworks and regulations.
Upon passing the certification exam, participants can apply for the “PECB Certified Lead AI Risk Manager” credential.
Who Should Attend?
This training course is intended for:
- Professionals responsible for identifying, assessing, and managing AI-related risks within their organizations
- IT and security professionals seeking expertise in AI risk management
- Data scientists, data engineers, and AI developers working on AI system design, deployment, and maintenance
- Consultants advising organizations on AI risk management and mitigation strategies
- Legal and ethical advisors specializing in AI regulations, compliance, and societal impacts
- Managers and leaders overseeing AI implementation projects and ensuring responsible AI adoption
- Executives and decision-makers aiming to understand and address AI-related risks at a strategic level
Learning Objectives
Upon successfully completing the training course, participants will be able to:
- Understand AI risk management fundamentals, including key concepts, approaches, and techniques for identifying, assessing, and mitigating AI-related risks
- Identify, analyze, evaluate, and treat AI risks, such as bias, security vulnerabilities, transparency issues, and ethical concerns
- Develop and implement risk mitigation strategies and incident response measures to address AI-related threats and vulnerabilities
- Apply established AI risk management frameworks, such as the NIST AI Risk Management Framework and the EU AI Act, to ensure governance, compliance, and ethical AI use
Educational Approach
- The training course combines theoretical knowledge with practical applications, using real-world examples to illustrate the identification and mitigation of AI risks.
- The course includes various interactive activities, such as scenario-based exercises and multiple-choice quizzes, designed to deepen understanding of AI risk management principles.
- Participants are encouraged to engage in discussions and collaborate during exercises and quizzes.
- The quizzes are structured similarly to the certification exam, helping participants familiarize themselves with the exam format and key concepts.
Prerequisites
The main requirements for participating in this training course are having a fundamental understanding of AI concepts and a general knowledge of risk management principles. Familiarity with AI governance frameworks, such as the NIST AI Risk Management Framework or the EU AI Act, is beneficial but not mandatory.
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