What challenges did BeeKeeperAI face before AI implementation?

Healthcare organizations and AI developers face persistent barriers to validating and deploying AI because access to sensitive data is limited by governance, privacy, cybersecurity, and intellectual property concerns. Traditional collaboration models slow innovation and make it difficult to generate evidence on real-world populations. Months spent negotiating access, securing approvals, and establishing protections for sensitive data and intellectual property often delay or prevent promising AI initiatives from reaching patients and providers.

What was the impact of the AI solution?

In early production deployments, EscrowAI has helped reduce project contracting and approval timelines from a typical 9–12 months to less than 3 months. A five-site research collaboration avoided an estimated $1.5 million in costs and 9-months of lost research time spent de-identifying data. Real-world model performance evidence was generated in under two weeks. The model enables healthcare organizations to participate more directly in AI development and commercialization while maintaining control of data and governance.

Source

Microsoft

BeeKeeperAI EscrowAI: secure agentic AI validation on protected healthcare data using Azure Confidential Computing and Confidential Ledger

Use case typeAI governanceUpdated Jul 6, 2026

Healthcare organizations and AI developers face persistent barriers to validating and deploying AI because access to sensitive data is limited by governance, privacy, cybersecurity, and intellectual property concerns. BeeKeeperAI developed EscrowAI on Microsoft Azure to create a governed collaboration model in which encrypted models and encrypted datasets interact only inside Trusted Execution Environments. EscrowAI enables protected AI workloads, including machine learning, analytics, large language models, and agentic AI, to execute against sensitive datasets while remaining inside the data custodian's Azure environment.

Organization: BeeKeeperAI
Industry: Healthcare
Location: United States
Published: July 2026

Reported outcomes

1,500,000 USD

research collaboration direct costs avoidedCost savings

9 months to <3 monthsproject contracting and approval timelines9 monthslost research time spent de-identifying data2 weeksmodel performance evidence generation time

Strategic outcomes

Risk & complianceProtected patient data and model IP during validationRisk & complianceAccelerated AI commercialization timelinesEcosystem & partnershipsEnabled secure multi-party research collaborationRisk & complianceCreated verifiable evidence trails for each run

Primary read

Use case focus

Showing 2 of 2

1AI governance
2AI model training

Healthcare organizations and AI developers face persistent barriers to validating and deploying AI because access to sensitive data is limited by governance, privacy, cybersecurity, and intellectual property concerns.
Traditional collaboration models slow innovation and make it difficult to generate evidence on real-world populations.
Months spent negotiating access, securing approvals, and establishing protections for sensitive data and intellectual property often delay or prevent promising AI initiatives from reaching patients and providers.

EscrowAI uses Trusted Execution Environments powered by Azure Confidential Computing so models can compute on sensitive data while preserving privacy, governance, and intellectual property protections.
Algorithm developers encrypt their models locally and upload them for deployment into a Trusted Execution Environment, while the data custodian encrypts the dataset locally and keeps it in its own Azure tenant.
Only a confidential report leaves the environment after the run is complete, and each run is recorded in Azure Confidential Ledger.
The workflow uses Azure Blob Storage, Azure DevOps, Azure Kubernetes Service, and Azure Marketplace in addition to Azure Confidential Computing and Azure Confidential Ledger.

Technologies

Azure Confidential Computing Azure Confidential Ledger Azure Blob Storage Azure DevOps Azure Kubernetes Service Azure Marketplace

In early production deployments, EscrowAI has helped reduce project contracting and approval timelines from a typical 9–12 months to less than 3 months.
A five-site research collaboration avoided an estimated $1.5 million in costs and 9-months of lost research time spent de-identifying data.
Real-world model performance evidence was generated in under two weeks.
The model enables healthcare organizations to participate more directly in AI development and commercialization while maintaining control of data and governance.

Architecture

EscrowAI runs within each participating organization's Azure tenant. Developers encrypt models locally and upload them into Trusted Execution Environments powered by Azure Confidential Computing, while data custodians encrypt datasets locally and keep them in their own Azure tenant. The model and data interact only inside the protected environment, and only a confidential report leaves after execution. Each run is recorded in Azure Confidential Ledger; Azure Blob Storage, Azure DevOps, Azure Kubernetes Service, and Azure Marketplace support storage, orchestration, deployment, and procurement.

Sources & evidence1

Groundedness: 5/5Type: Customer StoryPublished: Jul 6, 2026Publisher: MicrosoftEvidence: PrimaryConfidence: High

Primary source

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Customer evidence