Description:

This course introduces participants to AI risk management and governance using the NIST AI Risk Management Framework (AI RMF). Learners will explore how to identify, assess, and mitigate risks associated with AI systems, including issues related to bias, transparency, security, and compliance.

Through real-world scenarios and practical examples, participants will learn how to align AI initiatives with organizational governance structures, implement risk management processes, and ensure responsible and trustworthy AI deployment.

Duration: 

1 Day

Course Code: BDT552

Learning Objectives:

After this course, you will be able to:

1. Understand the NIST AI Risk Management Framework (AI RMF)
2. Identify and categorize risks in AI systems
3. Apply governance, mapping, measurement, and management functions of AI RMF
4. Evaluate AI systems for bias, fairness, and transparency
5. Implement risk mitigation and control strategies
6. Align AI practices with regulatory and ethical requirements

AI practitioners, risk managers, compliance officers, data scientists, IT leaders, and professionals responsible for AI governance and responsible AI adoption

Basic understanding of AI/ML concepts and organizational risk management practices

Course Outline:

Module 1: Introduction to AI Risk and Governance

1. What is AI risk
2. Importance of governance in AI systems
3. Overview of global AI regulations and standards

Module 2: Overview of NIST AI Risk Management Framework (AI RMF)

1. Core functions: Govern, Map, Measure, Manage
2. Framework structure and key concepts
3. Trustworthy AI characteristics

Module 3: Identifying and Mapping AI Risks

1. Types of AI risks (bias, security, privacy, operational)
2. Risk identification techniques
3. Contextualizing AI use cases

Module 4: Measuring and Evaluating Risks

1. Risk assessment methodologies
2. Metrics for fairness, accuracy, and robustness
3. Testing and validation approaches

Module 5: Managing and Mitigating Risks

1. Risk mitigation strategies
2. Governance controls and policies
3. Monitoring and continuous improvement

Module 6: Implementing AI Governance in Organizations

1. Roles and responsibilities
2. Integrating AI RMF into business processes
3. Documentation and audit readiness

Module 7: Ethics, Compliance, and Future Trends

1. Ethical considerations in AI
2. Regulatory landscape and compliance
3. Emerging trends in AI governance