AI Governance in GxP Environments: 5 Pillars for Validation and Compliance
Artificial intelligence is increasingly being considered for use across regulated pharmaceutical and life sciences environments. However, AI presents challenges that traditional computer system validation approaches were not originally designed to address.
Traditional validation typically assumes that software behaves predictably and consistently. Artificial intelligence, particularly machine learning and generative AI, can challenge these assumptions through probabilistic outputs, model drift and changing behaviour over time.
As AI adoption grows, organisations need to think beyond simply asking whether an AI-enabled system has been validated.
They also need to consider how that system is governed throughout its lifecycle.
Effective AI governance in GxP environments requires a structured approach to risk, validation, data integrity, human oversight and ongoing monitoring.
Is AI the Risk – or Is It the Decisions Made Using AI Outputs?
One of the most important questions when introducing AI into a regulated environment is where the real compliance risk lies.
AI models can introduce risks including inaccurate outputs, bias, insufficient transparency and changing performance. However, the greater risk may arise when organisations rely on those outputs to make decisions affecting product quality, patient safety or data integrity without appropriate controls.
Consider a hypothetical scenario in which an AI-enabled tool is introduced to review electronic batch records and identify potential deviations before batch release.
The system is trained using historical batch data and configured to identify unusual trends, missing entries and potential compliance issues.
Over time, users become increasingly confident in the system and reduce the level of human review for batches where the AI identifies no issues.
A new type of process deviation then occurs that was not adequately represented in the historical training data. The AI fails to identify the issue and, because human oversight has also been reduced, the discrepancy progresses further through the process.
The primary governance failure in this scenario is not simply that the AI produced an incorrect result.
It is that the organisation allowed the AI system to move from decision support towards decision making without adequate oversight and controls.
In GxP environments, accountability must remain clearly defined. AI-generated outputs need to operate within a governance structure where appropriate human review, escalation and approval remain in place.
The objective should not be simply to trust or mistrust AI.
The objective is to ensure that AI-assisted decisions remain transparent, traceable and justifiable.
Why Does AI Require a Different Validation Approach?
Traditional computer system validation is generally designed around deterministic software behaviour: defined inputs are tested against defined expected outputs.
AI can challenge this model.
Depending on the technology and intended use, validation may need to consider factors such as:
- Accuracy and performance
- Training and test data quality
- Bias
- Model behaviour and limitations
- Hallucination or inaccurate output
- Human oversight
- Traceability
- Model drift
- Change control
- Ongoing monitoring
This changes the validation question.
Instead of asking only:
“Is the output always the same?”
organisations may also need to ask:
“Can we trust the process that generates, reviews and governs the output?”
AI validation therefore needs to focus on intended use, risk management, system performance and governance as well as traditional system functionality.
For validation teams, this represents a shift from validating software functionality alone towards establishing confidence in the trustworthiness of AI-assisted processes and decisions.
What Does Draft EU GMP Annex 22 Mean for AI in Critical GMP Applications?
The developing regulatory landscape is also reinforcing the need for strong AI governance.
Draft EU GMP Annex 22 provides specific guidance for the use of Artificial Intelligence in GMP-regulated computerised systems.
Importantly, the current draft takes a cautious approach to critical GMP applications.
For systems with a direct impact on patient safety, product quality or data integrity, the draft focuses on static AI models with deterministic outputs. Dynamic models that continuously learn during use, probabilistic-output models, Generative AI and Large Language Models are not covered for critical GMP use and should not be used for these critical applications.
For non-critical GMP applications involving Generative AI or Large Language Models, the draft emphasises the importance of appropriately qualified personnel reviewing outputs and ensuring they are suitable for their intended use.
This reinforces a fundamental governance principle:
AI cannot replace GMP accountability.
The level of validation and governance required should always be proportionate to the system’s intended use and the potential impact of an incorrect output or decision.
Building an AI Governance Framework for GxP Environments
Organisations preparing to introduce or expand the use of AI should establish a structured governance framework.
Five areas are particularly important.
1. Risk Classification
The first step is understanding exactly how the AI will be used and determining its potential GxP impact.
Risk classification should consider:
Purpose: Determine the GxP impact of the AI application and the level of regulatory oversight required.
Key considerations: Intended use, impact on patient safety, product quality and data integrity, and whether the AI is supporting a human decision or making decisions within a process.
Clear classification allows the organisation to apply controls that are proportionate to the actual risk.
2. AI Validation Strategy
The validation strategy should establish confidence that the AI-enabled system performs as intended within its defined use.
For AI, this may require a broader range of evidence than traditional functional testing alone.
Purpose: Establish confidence that the AI system performs appropriately within its approved intended use.
Key considerations: Requirements, challenge testing, performance and accuracy, consistency where applicable, hallucination testing, acceptance criteria and traceability.
Testing should be designed around the risks presented by the specific AI application rather than applying a generic validation approach.
3. Data Governance
AI performance is fundamentally dependent on data.
Organisations therefore need to understand the origin, quality, suitability and lifecycle of data used to develop, test and operate an AI-enabled system.
Purpose: Ensure data used by AI systems is reliable, controlled and compliant.
Key considerations: Data integrity, data lineage, training data quality, bias assessment, ALCOA+ principles, retention and security.
Without effective data governance, confidence in the AI output becomes difficult to establish regardless of the sophistication of the technology.
4. Human Oversight
Human oversight is one of the most important controls when AI-generated outputs influence GxP activities.
Roles and responsibilities should be clearly defined, including who reviews AI outputs, who has authority to approve decisions and what happens when an output is uncertain or challenged.
Purpose: Maintain accountability for decisions influenced by AI.
Key considerations: Human-in-the-loop review, approval workflows, escalation processes, user training and clear ownership.
The aim is not to remove the benefits of automation but to ensure appropriate expert judgement remains part of the process where required.
5. Continuous Monitoring
Validation should not end when an AI-enabled system goes live.
AI performance may need to be monitored throughout the system lifecycle to identify deterioration, changing behaviour, inappropriate outputs or other factors that could affect the validated state.
Purpose: Monitor AI performance throughout its lifecycle and respond appropriately when conditions change.
Key considerations: Model drift, performance metrics, periodic review, revalidation triggers, change control and incident management.
Monitoring requirements should be defined as part of the original governance and validation strategy rather than introduced only after a problem occurs.
Preparing for Future Regulatory Expectations for AI in GxP
Traditional validation seeks to establish a high degree of confidence and control.
AI can introduce additional uncertainty.
As a result, governance becomes as important as validation.
Organisations preparing for evolving regulatory expectations should focus on five fundamental principles.
Transparency: The AI system, its intended use, relevant data sources and the way AI-generated outputs contribute to decisions should be clearly documented and capable of being reviewed during audits or inspections.
Explainability: Users should have sufficient information to understand AI outputs and justify decisions made using them.
Accountability: Roles and responsibilities should be clearly defined so that there is always an identified owner responsible for the governance, performance and compliance of the AI-enabled system.
Risk management: A structured, risk-based approach should be applied throughout the lifecycle to identify, assess, mitigate and monitor potential impacts on patient safety, product quality and data integrity.
Human oversight: Appropriate human review and approval processes should remain in place so that critical decisions are not transferred to AI without adequate control and expert judgement.
AI Governance and Validation Need to Work Together
AI introduces new opportunities for regulated organisations, but it also changes the way validation teams need to think about software behaviour, risk and accountability.
A strong approach to AI governance in GxP environments should combine validation with risk classification, robust data governance, human oversight and ongoing lifecycle monitoring.
The objective is not to eliminate every element of uncertainty associated with AI.
It is to establish a controlled environment in which the organisation understands the technology’s intended use, knows its limitations, can monitor its performance and can demonstrate how AI-assisted decisions remain compliant, traceable and accountable.
Need Support with AI Validation and Governance?
Introducing AI into a regulated environment can require a different approach to traditional computer system validation.
Dataworks supports pharmaceutical and MedTech organisations with risk-based Computer System Validation (CSV), Computer Software Assurance (CSA), data integrity and governance for modern and AI-enabled systems.
Book a Validation Consultation
Or download our CSA & AI Validation Guide to explore practical considerations for applying risk-based validation approaches to AI-enabled systems.
