What are the best practices to implement confidential computing in production?

Confidential Computing protects your data from even the GPU provider accessing it. What are some best practices to learn while building POCs and production systems at scale for enterprises? Few practices comes to mind: \- Used minions and setup smaller models in TEE and secure, and smaller model holds the users document and speaks with the larger model without exposing the data. \- Signature between CPU and user's machines before letting SSH access. \- Even after SSH access, keeping all info (Docker files, installation packages etc) stored in compiled binaries so that an attacker cannot see the models, versions, and any other info even if they're able to SSH. Would love to learn from the community and anyone who have done confidential computing as an inference provider.