Confidential Computing on CPU and GPU Systems: How AI Data Centers Protect Data in Use
Modern AI runs on shared, high-performance infrastructure that processes enormous volumes of sensitive data and valuable model weights. In these environments, capacity is often swapped rapidly between different customers, which raises a hard question. How do you keep data and models private while they are being computed on hardware you may not fully control?
Encryption already protects data when it is stored and when it moves across a network. The stubborn gap has always been data in use, the moment it is loaded into memory and processed. Confidential computing exists to close that gap.
This article surveys how confidential computing works across CPU and GPU systems, why heterogeneous setups are the current frontier, and where the real boundary of trust actually sits.
The Three States of Data and the Hardest One
Security teams usually think about data in three states. Data at rest lives in storage. Data in transit...
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