In this episode, I speak with Chester Leung, co-founder and head of platform architecture at Opaque Systems. Chester shares his journey from a non-technical college student who stumbled into computer science at UC Berkeley to his involvement in cutting-edge data privacy and security research. His story highlights how early academic experiences, including collaboration with renowned faculty and industry partners, led to the creation of Opaque Systems and their groundbreaking work in confidential computing.

We explore what confidential computing actually is—how it allows organizations to analyze and derive insights from sensitive data without exposing it. Chester explains how Opaque’s platform empowers data scientists and engineers to process encrypted data and run machine learning workloads securely and efficiently, all while preserving user privacy and meeting stringent compliance demands.

Chester also discusses the importance of building a resilient team and fostering a culture of ownership and customer-centric thinking. He opens up about the challenges of educating the market on a relatively new concept and the lessons learned from merging academic innovation with real-world commercial needs. Whether you’re curious about the next frontier of data privacy, the practical applications of confidential computing, or the crucial role of trust and integrity in emerging AI systems, this conversation shines a spotlight on the future of secure, privacy-preserving artificial intelligence.

Connect with Chester:
linkedin.com/in/chester-leung

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Connect with me on LinkedIn:
linkedin.com/in/gregtoroosian