UC Davis

Modern biomedical AI pipelines require robust data protection across heterogeneous environments, including edge devices, hospital servers, and cloud resources, each with distinct performance, trust, and regulatory considerations. While recent advancements in hardware-backed confidential computing (e.g., Intel SGX, AMD SEV, ARM TrustZone) offer promising solutions for data security, their differing threat models prevent seamless, end-to-end "capture-to-use" protection. To address this, we propose a novel, hardware-agnostic security monitor that extends the attestation and memory-encryption capabilities of these disparate Trusted Execution Environments (TEEs). This is complemented by a software-defined secure tunnel that enforces data-centric policy, provenance, and compliance. Our proof-of-concept prototype, integrating a TrustZone-enabled Raspberry Pi with an AMD SEV virtual machine in a cloud environment, demonstrates a deployable, data-centric enclave architecture that achieves end-to-end confidentiality, integrity, and compliance without compromising clinical throughput in biomedical AI workflows.