The House of AI Trust™

A Five-Layer Model for Trustworthy AI in Regulated Drug Development Workflows

Download the free flagship guide: includes the 25-point organizational readiness checklist, regulatory timeline, and the control-layer framework (including the Britt Probabilistic Validation Lifecycle) that converts governance into evidence.

05 Business ROI Why we built it

04 Domain & process context Where it lives

03 The control layer Where validation happens

02 AI Trust How we manage it

01 Trust infrastructure The foundation

The house of AI trust

Five layers. One defensible system.

Each layer depends on the one beneath it. Hover or tap any layer to see what it covers — and why removing it collapses the floors above.

Layer 01 · Foundation

Trust infrastructure

Data governance and the regulatory landscape, working as co-equal pillars. You cannot validate an AI system if the data beneath it is ungoverned, and you cannot defend it if you do not know which regulations apply. Everything above rests on this.

Layer 02 · Management

AI governance

Policies, accountability structures, model inventories, risk tiering, and lifecycle ownership. Governance defines who is responsible for what — and aligns the organization to FDA, EMA, and emerging AI-specific regulatory expectations.

Layer 03 · Where validation happens

The control layer

The defensible core: validation protocols, error taxonomies, testing frameworks, monitoring architectures, change control, and human-in-the-loop design. This is what turns policy into evidence — and what an auditor actually inspects. It is the layer most organizations are missing.

Layer 04 · Context

Domain & process context

A validated model in the abstract is not a validated model in your workflow. This layer maps the AI system to the specific regulated process — CSV scope, GxP touchpoints, user roles, decision authority — so controls are tied to real operational risk, not generic checklists.

Layer 05 · Outcome

Business ROI

Validation only matters if it produces measurable value — cycle-time reduction, audit-readiness, fewer rework loops, faster approvals. Without ROI, governance is overhead. With it, governance becomes a competitive moat.

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Not ready to commit to the full guide? View my presentation on The Engineering of Uncertainty: Validating Probabilistic AI in a GxP World at ISPE CBA MAST on May 7th, 2026.

The House of AI Trust™

A Five-Layer Model for Trustworthy AI in Regulated Drug Development Workflows

The House of AI Trust — five stacked layers, from Trust Infrastructure foundation up to Business ROI. Hover any layer to see what it covers and why it matters.