Use AWS Nitro Enclaves and Duality’s Privacy Protected AI Solution to futureproof, secure, and scale data acquisition for model development, tuning, testing, and customization while protecting model IP and maintaining the privacy of data inputs.

The recent executive order from the US Whitehouse and the current NIST RFI for guidelines in developing and using AI securely while maintaining the privacy of data inputs have raised many questions. Fortunately, the combination of AWS’s Nitro Secure Enclaves with Duality’s Privacy Protected AI Solution provides critical answers to tough data questions facing AI Engineering Teams:

• How do we acquire data at the scale and diversity needed to move models quickly to production?
• How can we collaborate with 3rd parties to validate and tune our model on real data versus mock or synthetic data?
• How do we coordinate with data from multiple parties?
• How do we prove to clients that our model is valuable to them?
• How do we do all that while protecting our IP and the privacy of data?

Join Duality’s Head Data Scientist and AI Engineer, Zohar Duchin, and Sr Partner Solution Architect, Ashok Mahajan of AWS, to hear how this joint solution can help AI Engineering teams overcome data acquisition, training, inference, customization, and monetization challenges while protecting model IP and the privacy of data inputs.

• Security and Privacy Challenges in Data Acquisition
• Synthetic and Mock Data vs Real Data for Development & Tuning
• What ‘AI Collaboration’ means and why it’s important
• Multi-party Collaboration in AI Development
• How to secure and scale access to real data throughout the AI development lifecycle