AI / Quality / Software
AI validation framework
A validation approach for AI-enabled GxP applications, from intended use to monitoring.
Illustrative use caseCapability
AI Governance & AI Validation
AI is not implemented simply because it is available. In pharmaceutical work it needs an intended use, a risk assessment, validation and lifecycle control.
What we do
An AI system that touches GxP decisions, records or patient-relevant information is a computerised system with some unusual properties: outputs can vary, performance can drift, and training data shapes behaviour. Governance has to address all three.
We set up AI governance frameworks, assess individual AI use cases, and plan validation that is proportionate to risk — drawing on GAMP 5, the GAMP AI guidance, EU Annex 11 and emerging regulator expectations.
Where it fits in the pharmaceutical lifecycle
Typical activities
Each activity shows who performs it. We do not claim facilities we do not own.
DGXP-led Led by the DGXPComplianceLab team Partner-coordinated Performed by a qualified partner, coordinated by us
Intended use
DGXP-ledClear statement of what the AI does, for whom and with what decision authority.
Risk assessment
DGXP-ledImpact on patient safety, product quality and data integrity.
Data assessment
DGXP-ledProvenance, representativeness, bias and integrity of data.
Model assessment
DGXP-ledModel selection rationale, limitations and known failure modes.
Human oversight
DGXP-ledReview steps, override and accountability.
Validation & testing
DGXP-ledAcceptance criteria, test datasets and challenge testing.
Performance monitoring
DGXP-ledMetrics, thresholds and periodic review.
Change control
DGXP-ledModel, prompt and data changes under controlled change.
Documentation & auditability
DGXP-ledRecords that let an inspector follow the reasoning.
Security & data integrity
DGXP-ledAccess control, logging and ALCOA+ for AI records.
Lifecycle management
DGXP-ledFrom concept to retirement, including decommissioning.
AI is not simply implemented because it is available
Pharmaceutical AI needs the same discipline as any GxP computerised system — plus controls for variable outputs, data dependence and performance drift.
- Intended use
- Risk assessment
- Data assessment
- Model assessment
- Human oversight
- Validation
- Testing
- Performance monitoring
- Change control
- Documentation
- Auditability
- Security
- Data integrity
- Lifecycle management
AI lifecycle under governance
- 1AI idea
- 2Use case
- 3Risk assessment
- 4Design
- 5Development
- 6Testing
- 7Validation
- 8Deployment
- 9Monitoring
- 10Change control
Technologies and processes
- GAMP 5 (2nd ed.)
- ISPE GAMP AI guidance
- EU Annex 11
- EU AI Act awareness
- ICH Q9(R1)
- Model cards
- Test datasets
Example projects
Illustrative use cases showing how this capability is typically applied.
AI / Quality
AI quality risk assessment
Classifying proposed AI use cases by risk and defining the validation each one needs.
Illustrative use case
AI / Quality / Training
AI-based SOP knowledge assistant
An assistant that answers procedural questions from effective SOPs, with citations.
Illustrative use caseOur project execution model
Every engagement follows the same eight steps, scaled to the size and risk of the project.
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01
Understand
Understand business and technical requirements.
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02
Assess
Assess scientific, technical, regulatory and operational requirements.
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03
Design
Design the appropriate solution.
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04
Identify
Identify appropriate internal and external capabilities and partners.
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05
Execute
Coordinate and execute the project.
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06
Verify
Test, verify, qualify or validate where applicable.
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07
Document
Create technical, quality and regulatory documentation.
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08
Improve
Monitor, optimise and continuously improve.
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Frequently asked questions
Is there a regulation specific to AI in GMP?
Expectations are evolving. Existing computerised-system rules (Part 11, Annex 11, GAMP 5) apply today, and regulators have published AI-specific guidance and draft guidance that we track.
How do you validate a model whose output varies?
By defining acceptance criteria on representative test sets, testing boundaries and failure modes, and controlling the configuration so results remain reproducible within agreed limits.
What does an AI governance framework include?
Policy, roles, use-case intake and classification, risk assessment templates, validation approach, monitoring and change control.
Discuss a ai governance & validation requirement
Share the product, stage and timeline. Capability is confirmed after technical assessment and partner availability.