Council Post: Data Privacy Within Generative AI & Considerations For The Enterprise

As enterprises increasingly adopt generative AI, managing sensitive data, especially first-party consumer data akin to PII, becomes crucial. Ensuring data privacy and security within cloud platforms is the first step before diving into predictive or prescriptive analytics.

In a recent roundtable, Aravind Peddiboyina, AI Innovation & Global Analytics Delivery Leader at Kimberly-Clark, Senior Director at a Healthcare Company, Avijit Chatterjee, Head of AI/ML at Memorial Sloan Kettering Cancer Center, and Vinod Malhotra, CTO at Perceptyx, shared their insights.

They discussed the challenges of data storage, security policies, customer data usage rights, and compliance with regulations such as HIPAA. Emphasis was placed on transparency, ethical considerations, and the need for strict data management protocols in the evolving landscape of generative AI.

Managing Sensitive Data: Storage, Security, and Analytics in Cloud Platforms

I wanted to share with you that we handle a large amount of sensitive first-party consumer data, which is almost like personally identifiable information. This data is stored on cloud platforms, with analytics being the next step after storage. Security policies are often discussed before any analytics are performed. Currently, we use Azure primarily, but all our first-party data from retailers is stored on the Google platform. Each platform has its own regulations, security, and cyber guidelines.