Description
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.
Who is this training for ?
For whom ?
- 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
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.
Training objectives
Training program
- 1: Introduction to AI Risk Management
- Overview of AI concepts and key AI-related risks
- Ethical, regulatory, and organizational challenges of AI adoption
- Introduction to AI risk management principles and frameworks
- 2: Organizational Context, AI Risk Governance, and AI Risk Identification
- Understanding organizational context related to AI systems
- Establishing AI risk governance structures and responsibilities
- Identifying AI risks across the AI system lifecycle
- 3: Analysis, Evaluation, and Treatment of AI Risks
- Analyzing and assessing AI risks, including bias, security, and transparency
- Risk prioritization based on impact and likelihood
- Defining and implementing AI risk treatment and mitigation measures
- 4: AI Risk Monitoring, Reporting, and Performance Optimization
- Establishing AI risk monitoring and reporting mechanisms
- Training and awareness programs on AI risk management
- Continuous improvement and optimization of AI risk management performance
