Biotech: AI-Assisted Deviation Triage and CAPA Recommendation Engine
What we succeed?
As a result of the partnership we established with Validfor, our testing, approvals, documentation, repository in one platform and full visibility, traceability and audit rediness.
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The Problem
Unvalidated AI Decision Support Introducing Bias Risk into GxP Investigation Workflows
A biotech deploys an AI tool that suggests deviation categorization and CAPA recommendations, with QA making final decisions. Without defined intended use boundaries and validated performance, AI output bias poses medium-high risk to investigation integrity — and model updates without change control create critical data integrity exposure.
Medium-high risk if AI output biases deviation investigation or CAPA categorization
No defined intended use boundaries preventing autonomous CAPA closure
Model updates deployable without documented impact assessment or regression testing
AI recommendations not traceable to model version or auditable input data
The Strategy
GxP-Compliant AI Validation with Defined Use Boundaries, Drift Monitoring, and Change Control
The validation scope covered the AI tool as GxP decision support — including auditability of recommendations, version control, and ongoing monitoring. Performance was validated on curated scenarios with documented failure modes and human override requirements, ensuring the system operates within defined and auditable boundaries.
Define intended use boundaries — no autonomous CAPA closure permitted
Validate performance on curated scenarios; document failure modes and human override mechanisms
Implement drift monitoring and change control triggers for model updates
Establish version control and auditability of all AI-generated recommendations
Validfor is an outstanding solution for managing the software validation life cycle. It brings structure and clarity to the entire validation process.
Luca Termite
IT Project Manager
The Solution
Auditable AI Recommendation Engine Operating Within Validated GxP Boundaries
Every AI recommendation is traceable to the model version and input data, with QA review recorded at each decision point. Model updates are governed by documented impact assessment and regression testing — ensuring the system remains in a validated state and cannot introduce uncontrolled bias into GxP investigation workflows.
Recommendations traceable to model version and input; QA review recorded for every decision
Model updates require documented impact assessment and regression testing before deployment
Drift monitoring in place to detect and trigger change control when model performance degrades
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