AI Governance: Risk, Compliance & Responsible AI [2026]

About this course

If you’re an AI team lead, product or innovation professional, legal or compliance specialist, IT professional, or anyone involved in AI strategy, development, deployment, or governance, you’re likely facing a difficult question: How do you enable AI innovation without losing control of the risks?

How do you decide which AI systems need deeper review? What data is going into your AI? Who owns the governance decision? How should you evaluate ChatGPT, Claude, or other third-party AI tools? What changes when AI can access enterprise systems or take actions autonomously?

AI Governance: Risk, Compliance & Responsible AI in Practice gives you a practical framework for answering these questions and putting AI governance into action.

You’ll move beyond abstract principles and explore how to build an AI governance approach across the AI lifecycle—from identifying and classifying AI systems to assessing risk, governing data, applying responsible AI principles, navigating regulatory approaches, establishing accountability, managing third-party AI, securing AI systems, responding to incidents, and governing AI agents.

In this course, you will:

• Build a practical AI governance framework that can be applied across different AI use cases and organisational contexts.
• Develop an AI inventory and lifecycle approach to create visibility into how AI is being used across your organisation.
• Assess AI risks and apply risk-based tiering so that governance effort is proportionate to the potential impact.
• Evaluate AI data practices, including data quality, bias, traceability, and the information entering AI systems.
• Apply responsible AI principles through practical controls for explainability and meaningful human oversight.

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