About the webinar
AI is creating new opportunities across healthcare, research, and many other domains, but working with sensitive data also brings significant privacy and security challenges. How can we take advantage of AI while keeping sensitive information protected?
In this webinar, we will share practical experiences and lessons learned from working with AI systems involving sensitive data. We will briefly introduce key privacy and security risks, discuss practical approaches to reducing these risks, and highlight some of the challenges organizations need to consider when deploying AI in real-world settings.
The webinar will provide an overview of these challenges and offer some practical perspectives for thinking about privacy, security, and trust when working with AI.
Who is the webinar for?
This webinar is intended for researchers, AI/ML practitioners, start-ups, companies, technology professionals, and decision-makers interested in the privacy and security challenges of using AI with sensitive data. It is particularly relevant for organizations that are developing, evaluating, or planning to deploy AI systems involving personal or sensitive information.
Key takeaways for participants:
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Understand the main privacy and security risks when AI systems work with sensitive data.
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Learn about practical approaches to reduce these risks throughout the AI lifecycle.
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Develop a clearer perspective on what to consider when building or adopting trustworthy AI systems.
Speaker bio:
Depeng Chen is a staff scientist working on AI security, privacy-preserving machine learning, and trustworthy. He holds a Ph.D. in Computer Science from Autonomous University of Barcelona. His work focuses on protecting sensitive data and addressing practical security and privacy challenges in AI systems.