Private AI Review

A practical checklist for pressure-testing vendor claims before buying.

Defending APIs requires layered controls at training, serving, and boundary levels working together.

Infrastructure must enforce where data lives, not just policy documents.

GPU TEEs push confidentiality into the compute layer where model training actually happens.

Encrypting multi-GPU training data costs speed; here's where and how much.

NVIDIA's closed-source GPU driver creates a security blind spot in confidential computing.

Researchers detail how CPUs and GPUs authenticate each other under AMD's confidential computing.

How to verify GPU identity and firmware integrity before trusting confidential workloads.

Six sequential layers, not one bottleneck, determine confidential inference cold start cost.

SGX's memory ceiling makes confidential VMs the safer choice for large models.