Confidential AI runtimes protect data and model state while in use through hardware-backed isolated execution and remote attestation. They address threats from a host, hypervisor, administrator, or neighboring workload that ordinary encryption at rest and in transit does not cover.
Key takeaways
- Attestation establishes what code and configuration is running before a client releases secrets.
- Confidential computing narrows infrastructure trust but does not make model behavior correct or safe.
- Operational details—debug mode, firmware, key binding, logging, crash handling, and wiping—determine the real boundary.
Threat model
Define whether the threat includes cloud administrators, host kernel, hypervisor, physical access, malicious tenant, supply chain, or compromised application code. A TEE protects only within its documented boundary. A vulnerable tool adapter running inside the enclave can still disclose data through an allowed channel.
Trusted execution
A trusted execution environment uses hardware isolation and memory protection so plaintext is available only inside a measured execution boundary. The Confidential Computing Consortium documents the broader model and terminology. [ar_cite id=”confidential-computing” label=”Confidential Computing Consortium”]
Remote attestation
Attestation evidence binds measurements, security configuration, platform identity, and freshness. A verifier evaluates evidence against policy and returns a decision or signed token. The client should verify the chain, expected measurements, debug state, and expiry before sending protected input.
Key binding and session setup
Session keys should be generated or unwrapped inside the trusted boundary and cryptographically bound to attestation evidence. Otherwise a valid attestation can be relayed while a different endpoint terminates encryption. OpenPCC explores an open confidential LLM-serving design with attestation and key binding across CPU and GPU boundaries. [ar_cite id=”openpcc” label=”OpenPCC”]
Confidential accelerator execution
GPU confidential-computing modes protect device memory and links under supported configurations. [ar_cite id=”nvidia-confidential” label=”NVIDIA documentation”] The runtime must verify driver, firmware, device state, peer-to-peer path, and any feature disabled or changed under confidential mode. Logs, metrics, model caches, and crash dumps require separate review.
Limits
- Does not prevent prompt injection, hallucination, or authorized misuse.
- Does not prove the application’s policy is appropriate.
- May add startup, memory, debugging, and performance constraints.
- Requires a trusted verifier and measurement-management process.
- Side channels and output disclosure remain part of the threat model.
- Third-party tools and remote APIs can move data outside the enclave.
Deployment checklist
- Document the protected assets and excluded threats.
- Pin accepted measurements and firmware policy.
- Verify attestation freshness and key binding.
- Disable debug and insecure fallback in production.
- Minimize data, secrets, and lifetime inside the boundary.
- Define secure wipe, crash, snapshot, and support procedures.
- Benchmark the exact confidential configuration.