Governing Sensitive Pharma Data with a Semantic Layer

Practical steps for making clinical trial data safe to reuse at scale

Years of clinical trials, yet most of the data has never been reused. Each clinical study generates rich patient-level data. Once a study closes, the majority of that data remains locked inside trial data management systems, effectively invisible to the rest of the organization. It is siloed by study, system, and therapeutic area, with governance processes built for single use rather than reuse.

There's a better architecture.

Perspectives on AI, LLMs, and semantic technologies in the life sciences industry group

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Governing Sensitive Pharma Data with a Semantic Layer — ONTOFORCE Webinar

WHAT WE COVERED

In this webinar

We walked through the practical steps needed to make clinical data available for internal evidence generation without compromising patient privacy, regulatory compliance, or data integrity. Using a real pharma deployment of DISQOVER's semantic layer and knowledge graph, we showed exactly what each step looks like in practice.

We covered the architecture, the governance mechanics, and the real-world results, including how AI agents can operate via MCP, governed by the same rules as human researchers, by design.

The problem

Clinical trial data sits locked in study-specific systems, invisible to the rest of the organization

The approach

A semantic layer with governance built into the architecture, not layered on top of existing models

The result

Clinical data available for reuse across studies and therapeutic areas, with full access control by design

KEY TAKEAWAYS

What you'll learn from this recording

Why governance bolted onto data models breaks, and what built-in governance looks like instead
How access policy is modelled as a first-class entity, so every query — whether from a researcher or an AI agent — automatically operates within the correct permission boundaries by design
Why restricted data should never be silently hidden, and how transparency about suppression changes researcher behavior
How DISQOVER exposes governed MCP tools that enforce access policies regardless of which AI agent calls them
What it took to get there, including the design principles the team wished they'd had from day one

We showed how AI agents operate via MCP and are governed by the same rules as human researchers — by design, not by afterthought.