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.
Ian Wardell
Ian Wardell, JD is a privacy and security professional with expertise grounded in a deep understanding of the legal and technical aspects of privacy. His work focuses on real-world technical solutions, ensuring compliance and safeguarding data privacy across highly regulated sectors.
Before joining Gusto, Ian was involved in Artificial Intelligence Research at Intel, where he published conference papers, developed models, and contributed to industry ISO Standards for safer data utilization. Prior to this, Ian specialized in Intellectual Property and data protection at Mertzel Law PLLC, where he navigated complex legal landscapes to protect IP and data compliance. Ian has also worked as a manager, and high-performance compute systems administrator, at Rutgers University.
Ian holds a JD from Seton Hall University School of Law as well as a Masters in Cybersecurity and Bachelor's degrees in Computer Science and Political Science from Rutgers University. His academic research includes publications on Product Liability Applied to Automated Decisions, Client-Side Malware Scanning In 802.1x Network Authentication, and AI & Machine Learning in Anomaly Detection.