AI SYSTEM VALIDATION · GxP · PHARMA

Validate AI systems in GxP — from intended use to controlled operation.

We advise and support the implementation of bounded AI-enabled systems in GxP: from use-case and supplier decisions through control design, integration and testing to release and monitored operation. Scope, data, model, supplier, interfaces and human controls form one traceable validation package.

AI consulting, implementation support and validation for a defined GxP use. No generic AI transformation or custom foundation-model development.

Discuss an AI validation assessment See lifecycle controls
VALIDATION OBJECT

The model is only one part of the validated system.

A blanket statement that an AI model is validated is not sufficient. The validation object is the configured system in its operating context and for one approved intended use.

01

Intended use and GxP classification

We define the task, users, inputs, outputs, decisions, exclusions and effects on patient safety, product quality and data integrity.

02

System selection and supplier assessment

We translate intended use and risk into selection criteria, then assess model provenance, architecture, hosting, data handling, update policy, supplier evidence, auditability and operating boundaries.

03

Implementation and validation controls

We support requirements, data and interface controls, configuration, metrics, thresholds, human review, fallback, traceability and the operating procedures needed before testing and release.

04

Testing, release and monitoring

Representative evaluations, end-to-end transactions, interfaces, records and human interaction are tested. Release criteria, drift monitoring, incidents, changes and retest triggers remain controlled in operation.

LIFECYCLE CONTROLS

Which lifecycle controls are required?

  1. 1. Inventory and intended useOwner, system boundary, process, users, data, outputs, limitations and affected decision.
  2. 2. GxP impact and riskReasonably foreseeable failures and effects on patient safety, product quality and data integrity.
  3. 3. Supplier, design and dataArchitecture, provenance, configuration, data lineage, representativeness, independence and security.
  4. 4. Requirements and acceptance criteriaUse-specific metrics, thresholds, error classes, abstention, escalation and human review.
  5. 5. Verification and validationIndependent representative tests plus interfaces, records, access, audit trails, fallback and recovery.
  6. 6. Release and operationAccountability, training, approved configuration, monitoring, incidents, change control and retest triggers.

Typical validation package

  • Intended-use and GxP impact assessment
  • Supplier and system assessment
  • Risk assessment and validation plan
  • Requirements and traceability
  • Data and evaluation specification
  • Protocols, raw results and deviations
  • Validation report and release decision
  • Monitoring, change and retest plan
RISK CLASSIFICATION

How is an AI use case classified on a risk basis?

Classification starts with the real process impact. The decisive questions are what the system influences, how autonomous the output is, whether qualified personnel can detect an error before harm, and which records or decisions depend on the result.

A vendor category or the label 'AI' does not determine validation depth. A low-autonomy drafting aid and an automated quality decision require different controls even when they use the same underlying model.

Regulatory boundary: EU GMP Annex 22 is currently a consultation draft and not a final effective annex. It is a relevant forward-looking reference for GMP manufacturing, not a universal rule for all GxP contexts. The final FDA CSA guidance is scoped to software used in medical-device production or quality management systems.
FAQ

Frequently asked questions about AI validation in GxP.

How do you validate an AI system in GxP?

Starting from intended use and GxP impact, define the system boundary, risks, data, supplier, requirements, acceptance criteria and human controls. Independent representative tests, end-to-end verification, traceability, approved release, and monitoring, change and retest rules for operation follow.

Which lifecycle controls are required for AI?

At minimum: inventory and owner, intended use, GxP and risk classification, supplier and system assessment, data governance, approved acceptance criteria, independent testing, human oversight, configuration control, monitoring, incident management, change control and defined retest triggers.

How is an AI use case classified on a risk basis?

By the effect of a failure on patient safety, product quality, data integrity and the regulated process. Autonomy, detectability of error, human intervention, data criticality and the importance of the affected decision also matter. The specific use, not the model name, determines the depth.

Must every AI system be validated?

No. Intended use and GxP impact are decisive. If the system affects a GxP process, quality-relevant decisions or GxP data, it needs documented fitness evidence at a risk-appropriate depth. Purely non-GxP uses are not automatically subject to CSV.

Is Annex 22 already binding?

No. The publicly available text is a consultation draft and not yet a final effective EU GMP annex. It indicates regulatory direction for GMP-related AI projects. Its draft status must remain clear in assessment and communication.

Does DHC also support AI system implementation?

Yes, for bounded GxP use cases. DHC translates intended use and risk into system and supplier criteria, requirements, data and interface controls, testing, procedures, release and monitoring. Generic AI transformation and custom foundation-model development are outside the offer.

PRIMARY SOURCES

Regulatory basis used for this page

VALIDATION ASSESSMENT

Define the validation object before building the evidence.

In 30 minutes we clarify intended use, GxP impact and whether a paid validation assessment is the right next step.

Discuss the validation assessment