Governance and Risks of AI in Business

Artificial Intelligence is reshaping industries, but its adoption also introduces ethical, operational, and regulatory risks. This course introduces the core risk categories of AI in business and explores frameworks such as ISO 42001, ISO 23894, and the NIST AI Risk Management Framework. Participants will practice applying governance principles and accountability models to ensure trustworthy AI adoption. The training is designed for enterprise managers, IT professionals, and compliance officers seeking practical skills to evaluate and mitigate AI risks in real-world contexts.

This training will cover:

• Introduction: Why AI Risk & Governance Matter
  • Rapid AI adoption and consequences of failure
  • Why AI projects fail: lack of trust, transparency, governance
  • Real-world issues: bias, hallucinations, security breaches 
    
• The Risk Landscape of AI in Business
  • Ethical risks: bias, discrimination
  • Cyber risks: model poisoning, adversarial attacks, deepfakes
  • Operational risks: overreliance, oversight gaps
  • Legal & regulatory risks: liability, EU AI Act, global perspectives (US, EU, China) 
    
• Frameworks for Managing AI Risks
  • Standards & frameworks: ISO 42001, ISO 23894, NIST AI RMF
  • Mapping risks to controls and mitigations 
    
• Governance & Accountability
  • Trustworthy AI principles: transparency, fairness, accountability
  • Internal structures: ethics boards, risk & data governance committees
  • Roles across IT, compliance, and leadership
  • The breakdown of the Org Chart in the agent era: task- and workflow-based accountability 
    
• Business Resilience & What’s Next
  • Adaptive governance for fast-evolving AI
  • Human-in-the-loop by design
  • Multidisciplinary expertise for AI risk
  • Scenario-based preparedness for AI risk 
  • Continuous learning
    

On successful completion of this course, participants will be able to:

• Explain why trust, transparency, and governance are essential for successful AI adoption, using real-world examples of AI risks.
• Identify and categorize AI-related risks across ethical, cybersecurity, operational, and regulatory dimensions.
• Apply internationally recognized frameworks (ISO 42001, ISO 23894, NIST AI RMF) to structure an AI risk assessment.
• Develop practical takeaways and action-oriented frameworks that can be implemented in their own organizations to strengthen AI risk management and governance.

The course is delivered through a mix of short lectures and interactive case studies. Participants will work in small groups to analyse real-world scenarios, conduct mini risk assessments, and propose governance solutions.