TEE & Hardware Security

Confidential Compute for Clinical AI

Why confidential computing matters for clinical AI

Confidential computing protects data in use — during computation — not just at rest (encryption) or in transit (TLS). In a clinical AI context, this means the model training process itself runs inside a hardware-enforced trusted execution environment (TEE) that the hospital infrastructure operator cannot inspect, modify, or exfiltrate data from.

This is critical because the hospital IT administrator is a privileged insider. They have root access to the server. Without confidential computing, they could — intentionally or through compromised credentials — access training data, model weights, or intermediate computation states. The TEE eliminates this trust requirement.

Rapha Protocol TEE architecture

The edge appliance runs three layers of hardware security:

Attestation verification flow

Before any model code touches clinical data, the Rapha Network Orchestration Hub verifies:

  1. SGX/DCAP quote — Intel Data Center Attestation Primitives verify the enclave identity, including MRENCLAVE (code measurement) and MRSIGNER (signer identity).
  2. TPM quote — platform configuration registers (PCRs) are checked against expected values for the boot chain.
  3. Intel root CA — the attestation verifier cross-checks the DCAP quote against Intel's published root certificate authority material.
  4. API key hash — the node's API key SHA-256 must match the expected value registered during node onboarding.

Only when all four checks pass does the attestor sign a proof digest that authorises training and enables settlement through RaphaClearingVault on Polygon mainnet.

Fail-closed design

Every component in the attestation chain fails closed:

There is no "demo mode" path to production settlement. The clearing vault accepts only proofs signed by a configured trusted attestor after verified hardware evidence.