Portfolio Accelerator · Healthcare · Microsoft Azure
Healthcare AI Governance on Microsoft Azure: A Working Reference Implementation, Not a Slide Deck
A ~97%-complete, working AI governance control plane for Microsoft-stack health systems — built on real Azure infrastructure against synthetic clinical data.
problem
Why Health Systems Need an AI Governance Framework Built for Microsoft Azure
Health systems standardizing on Microsoft — Azure OpenAI Service, Copilot for Microsoft 365, Azure Health Data Services — inherit Microsoft's compliance documentation, not a deployed governance control plane. Documentation describes what Azure services CAN do; it doesn't answer whether your specific model registry, audit logging, and policy enforcement are actually configured and enforced.
Where HIPAA, Azure OpenAI Service, and Copilot for Microsoft 365 Intersect
Every AI touchpoint that can see PHI — a clinical summarization model, a Copilot prompt referencing patient data — needs to be inventoried, access-controlled, and logged the same way any other PHI-handling system is under HIPAA's technical safeguards.
The Gap Between Microsoft's Compliance Documentation and an Actual Deployed Control Plane
Microsoft's own compliance program covers the platform. Whether a given health system's model registry, data classification labels, and audit logging are actually deployed and enforced is a separate, unanswered question most organizations haven't closed.
demo
Inside the Accelerator: What We Actually Built on Azure
This page showcases Kriv AI's Healthcare AI Governance accelerator — a real, working proof-of-concept deployed on Microsoft Azure, exercised against synthetic and de-identified healthcare data. No real patient data is used.
Architecture: Model Registry, Audit Logging, and Policy Enforcement on Synthetic PHI
The accelerator runs a synthetic patient population through Azure Health Data Services (FHIR) into an Azure Databricks lakehouse with Unity Catalog governing schema-level PHI sensitivity tags and row-level security, mirrors curated data into Microsoft Fabric for analytics, and layers Microsoft Purview on top for sensitivity labeling, data-loss-prevention policy, and auto-labeling of PHI-adjacent content. A set of governed AI agents — built on Azure AI Foundry — answer domain-specific clinical-operations questions, each scoped to its own access-controlled index and logged for review.
The build is approximately 97% complete against its own internal project checklist: the data pipeline, governance labeling, and agent layer are deployed and exercised end-to-end; a handful of manual portal-level configuration steps (row-level security on downstream dashboards, a couple of Purview scan-ruleset toggles) remain, by design, as items a client's own IT team typically owns during a production rollout.
Governance Controls Mapped to HIPAA Safeguards and the NIST AI Risk Management Framework
Sensitivity labels, DLP policies, and auto-labeling rules are configured against a taxonomy built specifically for PHI, clinical notes, and provider data — the same categories HIPAA's technical safeguards and the NIST AI RMF both expect an organization to be able to name, locate, and control.
copilot
Microsoft Copilot Healthcare Compliance: Governing the Copilot Layer Itself
Microsoft Purview, DLP, and Copilot Prompt/Response Auditing for PHI
Copilot for Microsoft 365 needs the same governance treatment as any other system that can surface PHI in a prompt or response: sensitivity labeling on the underlying content, DLP policies that block PHI from leaving governed boundaries, and logging of Copilot interactions with PHI-adjacent content. The accelerator's Purview configuration — sensitivity label taxonomy, auto-labeling rules, and DLP policies — is built to extend directly to that Copilot governance layer, not just the data lakehouse.
differentiation
Why a Working Accelerator Beats a Big 4 Governance Deck
A Big 4 healthcare AI governance engagement typically produces a framework document and a roadmap. This accelerator is a deployed system: a real FHIR server loaded with a synthetic patient population, a governed lakehouse, Purview policies actually configured and triggering, and AI agents actually running — something a prospect can see and question in a working session, not just read about.
engagement
From This Demo to Your Azure Tenant: How the Engagement Works
Engagements start from this same architecture pattern — FHIR ingestion, lakehouse governance, Purview labeling/DLP, and a governed AI agent layer — adapted to your Azure tenant, your data domains, and your existing Microsoft 365/Copilot footprint.
Straight answers
Frequently asked questions about Healthcare AI Governance on Microsoft Azure: A Working Reference Implementation, Not a Slide Deck
What's actually in the healthcare AI governance accelerator demo?
A working Azure deployment: a FHIR server loaded with a synthetic patient population, an Azure Databricks lakehouse with Unity Catalog PHI tagging and row-level security, Microsoft Fabric analytics, Microsoft Purview sensitivity labeling and DLP, and a set of governed AI agents built on Azure AI Foundry — all deployed and exercised end to end.
Is any real patient data used in the demo?
No. The accelerator runs entirely on synthetic patient data generated for demonstration purposes. No real PHI is used anywhere in the portfolio build.
How does this map to HIPAA?
The Purview sensitivity label taxonomy, DLP policies, and Unity Catalog PHI tagging are built directly against the data categories HIPAA's technical safeguards expect an organization to classify and control — patient data, clinical notes, and provider data each get their own labeling and access rules.
Does this cover Microsoft Copilot governance specifically, or just the data platform?
Both. The same Purview sensitivity labeling and DLP policy layer that governs the data lakehouse is designed to extend to Copilot for Microsoft 365 — governing what Copilot can surface from PHI-adjacent content, not just how data is stored.
What does an engagement cost and how long does it take?
Engagement scope and cost depend on your Azure footprint and data domains — see our pricing page for our engagement floor and structure. A typical path starts with a scoped discovery phase before any production rollout.
Can we see the demo before signing anything?
Yes — we can walk through the live accelerator in a discovery call, including the governance dashboards and agent layer.
Ready to see the accelerator run against your data model?
Bring your requirements to a working session and we'll walk through the live system.
Book a Discovery Call