Confidential AI on sensitive clinical data

A healthcare AI provider needed to train and run models on real patient data across multiple institutions, but each provider was blocked from sharing that data — legally, contractually, and technically.

Framed a confidential-computing pattern on Azure — data and model IP each protected in isolated enclaves, with attested workloads and a clear audit trail — and packaged it as a joint offer with a defined pilot scope, success metrics, and a route to production.

A working PoC on real clinical datasets with cryptographic assurance for both data owners and the AI vendor, plus a repeatable commercial structure the vendor could resell to the next hospital system.