Managing AI Risk
Masterclass

Artificial intelligence has moved from experiment to everyday business tool faster than any technology before it, and the risks that come with it have grown just as quickly. Regulatory obligations are arriving, led by the EU AI Act. Incidents involving biased decisions, deepfake fraud and unreliable AI outputs are making headlines. Boards, regulators and stakeholders expect organizations to show they are in control of how AI is used. And the professionals responsible for managing these risks are often being asked to govern a technology they were never trained to understand, with frameworks that are still taking shape.

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This masterclass is built for the people who own or oversee risk, compliance and governance in their organizations — managers and leaders from risk management, compliance, legal, procurement, HR, operations and sustainability. No technical background is required. Over two intensive live days you will build a clear, practical understanding of how AI works and where its risks lie, and gain the governance frameworks, assessment tools and decision-making capability to manage AI risk effectively — helping your organization capture the benefits of AI while meeting its regulatory obligations and protecting its stakeholders.

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This Program will be divided into 3 core modules:

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Module 1 — Why AI Risk Is Now a Leadership Issue

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Why AI has become central to how organizations operate, and why managing its risks can no longer be left to technical teams alone. Understand in plain language how AI and generative AI systems work, why they fail, and where AI is already being used across your organization, often without formal oversight. Explore the strategic, operational and regulatory drivers raising the stakes, and the governance gaps most organizations may not know they have. To bring concepts to life, we will work through real-world incidents and a running case study, alongside group discussion across industries.

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Module 2 — Six AI Risk Domains, how they are evolving, and implications for decision-making

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1. Fairness and discrimination (e.g., AI in recruitment and credit decisions)

2. Privacy and data protection (e.g., GDPR, confidential data entered into public AI tools)

3. Security and misuse (e.g., prompt injection, deepfake-enabled fraud)

4. Reliability and accountability (e.g., hallucination, over-reliance on AI outputs, autonomous AI agents)

5. Third-party and supply chain AI (e.g., AI embedded in vendor products and services)

6. Environmental and social impact (e.g., data center energy and water use, workforce and human rights impacts)

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Module 3 — AI Risk Management Strategies

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Building the governance, processes and program that manage AI risk in practice. Understanding the risk landscape is necessary but not sufficient. This module covers how to translate that understanding into a structured, practical and defensible AI risk management program — one that meets regulatory requirements, supports business objectives and demonstrates accountability to regulators, boards and stakeholders.

The module is structured around three interconnected elements of a mature AI risk management program: regulation and frameworks (the EU AI Act, the NIST AI Risk Management Framework and ISO/IEC 42001), risk assessment and controls, and governance, culture and AI literacy. Participants leave with a practical risk assessment template and a 90-day action plan for their own organization.