AI Governance & Assurance · Regulated Drug Development

Validation assurance for probabilistic systems in regulated science — where reproducibility is bounded, the validated state is a moving target, and conventional computer system validation practices were not designed for the sources of risk these systems introduce.

Britt Biocomputing develops and publishes structured frameworks for governing AI in GxP-regulated environments. The frameworks below operate together and extend the FDA seven-step credibility framework, ICH Q9(R1), and the GAMP 5 / GAMP AI Guide lineage into territory those standards do not yet explicitly address.

01 / Frameworks

An integrated body of work

Flagship Framework

House of AI Trust™

Five-layer governance architecture

The umbrella framework: organizes AI controls in regulated drug development across five layers — from foundational context-of-use definition through model credibility, composite system controls, monitoring, and human accountability. The other three frameworks below operate within or alongside the House.

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HOUSE OF AI TRUST L5Business ROI investable L4Domain + Process Context useful L3Control Layer VALIDATION · MONITORING · HITL defensible L2AI Governance manageable L1Trust Infrastructure FOUNDATION possible
L3 is where Britt Biocomputing operates.
Four threads run through every layer: Security · Explainability · Communication · Supplier Qualification

Supporting frameworks

02
Probabilistic Validation Lifecycle
Seven-step execution model
Adapts the V-model to systems where reproducibility is bounded rather than absolute. Maps cleanly onto GAMP 5 lifecycle stages while extending them for probabilistic behavior, drift, and continuous verification.
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03
VALID Trust
Four-pillar supplier qualification
A framework for qualifying AI suppliers and inheriting validation evidence in regulated environments. Extends GAMP 5 supplier qualification into non-deterministic upstream components.
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04
Two-Dimensional Error Taxonomy
6 × 6 classification matrix
The first public framework for classifying probabilistic AI failures in regulated drug development by both error type and origin, mapped against GAMP 5, ICH Q9(R1), and the FDA seven-step credibility framework.
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02 / Insights

Latest writing

JULY 2026
Your AI Looks Fine. Until It Doesn't.
Self-driving cars and LLMs fail the same way — smoothly, until the error is alien. The unit of validation isn't the model; it's the model × harness × context-of-use. This piece argues the agent harness and the validation harness should be the same harness, with different views on top.
JUNE 2026
FDA Asked How to Govern AI in Clinical Trials. Here's What We Said.
Our public comment on FDA's AI-Enabled Clinical Trial Pilot RFI — arguing that early-phase dosing decisions are the hardest validation problem in drug development, and proposing calibration, perturbation testing, and conformal prediction as metric axes mapped to FDA's own process validation lifecycle.

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Working with Britt Biocomputing

AI visualization and risk-tiering assessments, framework co-design for AI governance, and credibility planning for probabilistic systems in GxP contexts. Work is sized to the consequence of error and to the maturity of the client's current posture.

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