In the rapidly evolving landscape of Generative AI (GenAI), third-party vendor risk and sensitive data leakage pose significant challenges for enterprises, some of which may result in significant fines. Similar to other third-party products, nearly all GenAI tools are susceptible to these risks, complicating the development process and necessitating robust privacy-enhancing technologies (PETs).

This session will explore innovative real-world implemented technical solutions to these challenges, focusing on real-time data masking and role-based permissions as effective strategies to safeguard sensitive information. We will highlight how pioneering companies, such as the startup Formal, are leading the way in implementing these technologies, and offer insights into their practical application and how auditing processes can detect data sharing.

For privacy professionals, the imperative to practice data minimization and safeguard sensitive information — whether for privacy, contractual, or regulatory reasons — is paramount. This talk will address practical guardrails for enabling GenAI development without compromising on privacy and provide a roadmap for navigating the complexities of data sharing in today's GenAI environment.