Emerging Frontier Hybrids
Confidential AI Workspaces
Confidential AI workspaces run models and data inside hardware-backed enclaves so sensitive inputs, weights, and outputs stay protected even from the host and cloud operator. They let teams use powerful AI on regulated data without moving it to a shared, observable environment. The work is in attestation, key management, and closing the side channels and misconfigurations that break the confidentiality promise.
要点
- Use trusted execution environments with remote attestation before sending any sensitive data.
- Keep encryption keys inside the enclave and never export them to the host.
- Minimize the trusted computing base by running only model and data code in the enclave.
- Measure and mitigate side-channel leakage from timing, cache, and memory access patterns.
- Audit enclave configuration and attestation policies as part of every release.
- Log access and usage without logging plaintext inputs or outputs.
- Plan for enclave memory and performance limits when sizing models and batch sizes.
- Treat the host and hypervisor as untrusted in both design and incident response.
常见问题
Are confidential workspaces fully private?
They raise the bar significantly, but side channels and bugs can still leak data.
Do I need special hardware?
Yes, confidential computing relies on CPU or GPU features that support trusted execution.
Can I run any model inside an enclave?
Only models that fit memory and performance limits; large models need careful sizing.
How do I verify the enclave is genuine?
Remote attestation checks the hardware and code identity before data is released.
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