Council Post: Data Privacy and Vendor Trust in the Age of Gen AI

As generative AI technologies like large language models (LLMs) become more prevalent, organizations across industries are grappling with how to leverage their potential while mitigating risks around data privacy, compliance, and intellectual property. The rise of powerful AI systems that can generate human-like text, code, and other content have opened up new opportunities but also raised critical concerns.

To explore strategies for responsibly adopting generative AI while protecting sensitive data and proprietary models, we convened a roundtable of distinguished experts and industry leaders. Our panelists included Sreenivas Gadhar, Vice President, Global Data and Analytics - Engineering and Delivery at Marsh McLennan, Arjun Srinivasan, Director - Data Science at Wesco, Elanchezhiyan (Elan) Panneerselvam, Sr. Director Data & Adv. Business Analytics at Ally, Anil Prasad, Head of Engineering - Software Products, Data and ML at Ambry Genetics and Ashwin Sridhar, Chief Product & Technology Officer at Common Sense Media.

The key topics discussed included data sharing challenges, data minimization and anonymization approaches, cloud adoption considerations, integrating on-premises and cloud data assets, and balancing the opportunities and risks of generative AI deployments.

Addressing Data Sharing Challenges in Predictive Modeling

These data sharing challenges are not new. The concerns always existed about how to share the data. Should it be shared in encrypted format or to mask it? It's not just about sharing data outside your organization; even within your organization, from production to lower environments, these challenges exist.