3 Key AI/ML Takeaways from the PETs Summit North America

Enveil CEO Ellison Anne Williams was fortunate to facilitate a panel discussion today on a topic that is quickly gaining visibility across a broad range of industries: privacy concerns relating to the use of AI/ML. Specifically, the panelists explored the intersection points between Privacy Enhancing Technologies (PETs) and machine learning — and how these technologies can deliver value for ML use cases. 

Here are our three quick takeaways from the conversation: 

1. Lead with the business value — When adopting AI/ML, start with the defining business value, not by chasing the technology
2. Privacy-Preserving Machine Learning (PETs + ML) is powerful, but it isn't an AI silver bullet — PPML addresses many challenges in the AI arena, including security and privacy relating to sensitive models and underlying training data, but these solutions and approaches must be used in the context of a broader modelOps, risk, and governance framework.
3. Regulation and policy for AI, PETs, and PPML should be suggestive and not prescriptive — Regulation and policy should guide toward broad technology 'satisfaction' but allow for technology evolution.

Thanks to all the panelists (Katherine Fick, Paul Lekas, Vaikkunth Mugunthan, and Eugenio Zuccarelli) for their insights, and to the Privacy-Enhancing Technology Summit North America for serving as the venue for this important discussion. 

Looking for more on PPML? Check out these resources: 

• Privacy Enhancing Technologies for Machine Learning — The Next Frontier of Secure Data Usage 
• Silicon UK In Focus Podcast: Privacy-Preserving Machine Learning
• Enveil Recognized in 2023 Gartner® Market Guide for AI Trust, Risk and Security Management