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Abstract Blue Light
Certified AI Risk and Governance Practitioner

Target Students​

Risk managers, compliance officers, and governance professionals focusing on AI.

Duration :  40 hours (5 days)
Learning Objectives

-Understand AI-related risks and governance frameworks.

-Develop AI risk management strategies.

-Ensure compliance with AI regulations and standards.

-Implement governance policies for AI systems.

Exam Codes: CAIRGP-301
Exam Options​
 

Online

In-Person

Exam Duration: 2 hours
Exam Formats​
 

Multiple-choice

Scenario Analysis

Passing Score​​: 70% 
Course Outline
​Module 1: Introduction to AI Risk and Governance
​

  • Understanding AI Risks

  • Overview of AI-Related Risks in Business

  • Types of AI Risks: Operational, Ethical, and Regulatory

  • Case Studies: Notable AI Failures and Their Consequences

  • AI Governance Frameworks

  • Introduction to AI Governance Models

  • Key Principles of Effective AI Governance

  • Comparing Global AI Governance Standards

Module 2: AI Ethics and Responsible AI
​

  • Ethical Considerations in AI

  • The Importance of Ethical AI Implementation

  • Addressing Bias and Fairness in AI Systems

  • Ensuring Transparency and Accountability

  • Building a Responsible AI Framework

  • Developing Ethical Guidelines for AI Development and Use

  • Implementing Responsible AI Practices in Organizations

  • Tools and Resources for Monitoring Ethical AI Compliance

Module 3: Regulatory Compliance in AI
'

  • Global AI Regulations and Standards

  • Overview of AI Regulations Across Different Jurisdictions

  • Key AI Compliance Requirements in Major Markets

  • The Role of Data Privacy in AI Compliance

  • Implementing AI Compliance Programs

  • Designing AI Compliance Programs

  • Integrating AI Compliance with Existing Business Processes

  • Monitoring and Reporting AI Compliance

Module 4: Risk Assessment and Management in AI
​

  • Conducting AI Risk Assessments

  • Identifying Potential AI Risks in Your Organization

  • Tools and Techniques for AI Risk Assessment

  • Prioritizing Risks and Developing Mitigation Strategies

  • Mitigating AI Risks

  • Implementing Risk Mitigation Strategies

  • Continuously Monitoring and Adapting AI Risk Management

  • Case Studies: Effective AI Risk Mitigation in Practice

Module 5: AI Governance in Practice
​

  • Developing AI Governance Policies

  • Crafting AI Governance Policies Aligned with Organizational Goals

  • Setting Up Governance Structures and Roles

  • Best Practices for Ensuring Governance Compliance

  • Implementing AI Governance Across the Organization

  • Rolling Out AI Governance Policies and Procedures

  • Training and Educating Employees on AI Governance

  • Monitoring and Evaluating AI Governance Effectiveness

Module 6: Advanced Topics in AI Risk and Governance
​

  • AI Risk in Emerging Technologies

  • Understanding Risks Associated with Advanced AI Technologies (e.g., Deep Learning, Generative AI)

  • Assessing the Impact of AI on Cybersecurity

  • Preparing for Future AI Risks and Challenges

  • Innovations in AI Governance

  • Leveraging Technology for Better AI Governance

  • The Future of AI Governance: Trends and Predictions

  • Case Studies: Pioneering AI Governance Initiatives

Module 7: Practical Implementation and Case Studies
​

  • Developing a Comprehensive AI Risk Management Plan

  • Hands-On Workshop: Creating an AI Risk Management Plan for Your Organization

  • Group Activity: Simulating an AI Risk Management Scenario

  • Presentations and Feedback

  • Case Studies and Best Practices

  • In-Depth Case Studies of AI Governance Successes and Failures

  • Lessons Learned from Leading Organizations

  • Final Presentations and Peer Feedback

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