Confidential AI Ends the Standoff Between Regulated Data and Top-Tier Models

Companies with regulated data often can’t send it to someone else’s datacenter to use the proprietary AI models they want. Model builders, meanwhile, can’t hand over the weights that make those models valuable. Both sides are protecting an asset they cannot afford to expose, and the standoff has limited what enterprises can do with their most sensitive records. Companies moved agentic workloads into production over the past two years, which raised the cost of the standoff.

Confidential AI offers another path. The model travels to the data and runs inside a protected environment. Neither the records nor the weights are exposed to anyone else, including whoever operates the hardware. That environment has to prove what it is running before either asset is released. A real enclave does four things a merely isolated workload does not. It proves itself first, protects every party from every other party, forms where the data already sits, and produces a record an auditor can check.

VAST Data built DataEnclave into a data platform its customers already run, which is a different starting point from standing up a separate confidential stack. Records stay in place, weights decrypt only inside an attested enclave, and every attestation and key release lands in a queryable record. For regulated buyers, the technology question is largely settled. The open questions are which shelved projects are worth reopening and what proof to demand from any vendor selling an enclave.

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TABLE OF CONTENTS
Summary
The Standoff That Stalls Enterprise AI
Why Moving the Data Stopped Working
The Enclave and What It Has to Prove
How VAST Data Approaches Confidential AI
The Decision in Front of AI Buyers

COMPANIES CITED
VAST Data