The data that could make your AI and analytics most valuable is often sensitive, regulated, siloed, or owned by another organization. Download the 2026 guide to learn how organizations can compute on that data without exposing it while maintaining control, sovereignty, and accountability.

• What are PETs?
  Privacy-enhancing technologies, or PETs, allow approved queries, analytics, and AI models to run on sensitive data while the underlying data remains encrypted or stays in its owner’s environment. Only the permitted result is released.
• Why do they matter now?
  Recent advances have moved PETs from “possible in principle” to practical deployment at scale. Organizations can now support confidential LLM inference, federated AI, encrypted search and analytics, and secure collaboration across institutions, clouds, and borders.
• What will you learn?
  Explore how FHE, confidential computing, federated analytics and learning, multi-party computation, and differential privacy work; how they can be combined; and how they support secure AI, data sovereignty, and regulated collaboration across financial services, healthcare, government, and defence.
• Which PETs fit your data?
  Use the guide to better understand the technologies and deployment approaches relevant to your data, analytics, and AI initiatives.