Regulated financial services group

A financial group wanted to train proprietary fraud-detection and credit models on customer data under two hard constraints: sensitive datasets and models could not be exposed to shared multi-tenant infrastructure, and auditors demanded provable control over who and what touched the data — including AI agents acting without human intervention.

Secure GPU enclaves with policy-controlled data flows, keeping proprietary models and datasets inside the group's perimeter through training and inference. Zero Trust identity extended to every user, service, and AI agent, with continuous verification producing an auditable access record. Digital Twin monitoring giving the security team real-time visibility into workload behavior.

The group trained and deployed high-value models without the models or training data leaving its controlled environment. Continuous cryptographic verification satisfied audit requirements on access and agent behavior, and the enclave architecture protected the group's core IP as a competitive asset.