AI Governance - Overview

AI Governance - Overview

Managing Risk, Trust, and Responsibility in the AI Era

Understand How AI Works, Why It Fails, and How Organizations Govern It Responsibly

Artificial Intelligence is rapidly becoming part of everyday business operations, from automation and analytics to generative AI assistants and autonomous decision-making systems. While AI creates enormous opportunities, it also introduces risks involving bias, misinformation, security, privacy, compliance, and loss of human oversight. To govern AI effectively, organizations must first understand how AI systems work, where their limitations come from, and why governance has become a critical business requirement.

This one-day course provides a practical introduction to AI and AI governance, beginning with the fundamentals of AI, Machine Learning, and Generative AI before moving into governance frameworks, risk management, responsible AI principles, and organizational controls. The course is delivered from a real-world industry perspective by an instructor with over 30 years of industry experience, focusing on practical implementation and current enterprise challenges rather than purely academic concepts.

Learning Outcomes

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

• Understand the fundamentals of AI, Machine Learning, and Generative AI
• Explain how AI systems learn and generate outputs
• Identify common AI risks, limitations, and failure scenarios
• Understand the purpose and importance of AI governance
• Recognize core principles of responsible AI
• Understand AI governance frameworks and organizational controls
• Identify governance challenges associated with Generative AI
• Apply foundational AI governance concepts within an organization

Prerequisites

• Basic understanding of business and digital technologies
• No programming or prior AI experience required

Course Outline

Introduction to Artificial Intelligence

• What is AI
• Types of AI systems
• Machine Learning fundamentals
• How AI models are trained
• Introduction to Generative AI and Large Language Models
• AI use cases across industries

Understanding AI Risks and Limitations

• Hallucinations and inaccurate outputs
• Bias and fairness concerns
• Privacy and security risks
• Data quality issues
• Explainability and transparency challenges
• Risks of overreliance on AI

Introduction to AI Governance

• What is AI governance
• Why AI governance matters
• Governance versus ethics and compliance
• Responsible AI principles
• Human oversight and accountability

AI Governance Frameworks and Controls

• AI policies and governance structures
• AI risk management concepts
• AI lifecycle governance
• Monitoring and audit considerations
• AI documentation and accountability

Governance for Generative AI

• Risks associated with Generative AI
• Prompt injection and data leakage
• Intellectual property considerations
• Acceptable use policies
• Governing enterprise AI adoption

Organizational AI Governance

• Roles and responsibilities
• Governance operating models
• Cross-functional collaboration
• Managing third-party AI risks
• Building a governance roadmap

Emerging Trends and Future Considerations

• Regulatory developments
• AI governance standards and frameworks
• Agentic AI and autonomous systems
• Future governance challenges

Wrap-Up and Q&A

• Key takeaways
• Practical governance recommendations
• Discussion and participant questions

Disclaimer

This course outline is intended as a general training framework and guideline only. The instructor reserves the right to modify, expand, reorganize, or adjust the topics and depth of coverage as deemed appropriate based on participant background, organizational requirements, industry developments, and time availability without prior notice.