Private Model as a Service: Zero-Trust Blueprint for Protecting AI Weights
summary
Private Model as a Service presents a production-ready reference architecture that keeps model weights encrypted and decrypts them only inside hardware-rooted TEEs (Intel TDX, AMD SEV‑SNP) with GPU memory protection for NVIDIA H100/B200. It composes Confidential Containers, Kata runtime, KServe and vLLM, uses a Key Broker Service for remote attestation and key release, and handles network-restricted sites with a relay for network-restricted environments and a developer workflow unchanged two-line manifest while ops configures attestation policies and key registration.
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chapters (8)
- 0:00Introduction
- 6:47Confidential computing stack
- 10:38Security personas
- 12:28Trust models
- 17:33Deployment flow
- 20:49Community and next steps
- 21:11Key takeaways
- 22:18Q&A
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