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Data & AI Mastery

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Meet the Host

Dr Raoul-Gabriel Urma

Dr Raoul-Gabriel Urma is an EdTech entrepreneur, author, and international speaker focused on transforming workforce development through data and AI.

As the Founder and Chairman of Cambridge Spark, Raoul leads efforts to upskill employees of top organisations, including M&S and the BBC, with cutting-edge training. He co-invented the patented EDUKATE.AI platform, which personalises learning for data skills.

A member of YPO and affiliated lecturer at Cambridge, Raoul is also pursuing the Owner/President Management Program at Harvard. His best-selling book, Modern Java in Action, has sold over 60,000 copies, reflecting his impact on technology and education.

Latest episodes

From Six Months to Three Hours: How AI Has Shrunk the Attack Window

Data & AI Mastery

From Six Months to Three Hours: How AI Has Shrunk the Attack Window

In this episode of Data & AI Mastery, host Dr Raoul-Gabriel Urma is joined by Ashish Rajan, CISO at TechRiot and author of *AI Security Engineering*, to explore how AI is reshaping enterprise cybersecurity, why traditional security approaches are being challenged, and what organisations can do to manage emerging risks while building the skills needed to stay secure.

No Get Out of Jail Free Card: Five Data Leaders on AI in Regulated Industries

Data & AI Mastery

No Get Out of Jail Free Card: Five Data Leaders on AI in Regulated Industries

This episode covers the FCA's AI Live Testing programme, the Consumer Duty as the policy anchor for responsible AI, and how Santander UK's Luke Pearce secured C-suite sponsorship to move faster on generative AI.

Divya Kesavan on AI Transformation in Banking: Beyond RPA and Automation

Inside the Algorithm

Divya Kesavan on AI Transformation in Banking: Beyond RPA and Automation

In this episode, Divya walks Jeremy through the shift from rule-based RPA to reasoning-driven AI and why Lloyds' enterprise AI blueprint rests on three layers: reasoning, context and control. She unpacks what it actually takes to move an agentic system from a working proof of concept to a tool trusted with real banking decisions, including a striking example of an AI agent that went badly off script.

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