Key takeaways
- AI-enabled systems introduce lifecycle risks that traditional validation packages may not address by themselves, including model and data change, probabilistic behavior, supplier-controlled updates, and performance drift.
- When AI supports a GxP activity, organizations need risk-based controls for human oversight, data integrity, traceability, change management, monitoring, and record retention based on intended use.
- OET AI Guardrails is designed to help organizations establish technical and procedural controls aligned with applicable GxP expectations and recognized industry frameworks.
Why life sciences AI validation in GxP struggles under scrutiny
Life sciences AI projects can run into trouble when they are built on two assumptions: that AI behaves like traditional software, and that existing computerized-system controls automatically cover every AI-specific risk.
Traditional computerized-system validation remains highly relevant. But existing controls may need to be extended when AI introduces probabilistic outputs, model or data changes, new supplier dependencies, performance monitoring needs, or additional governance requirements.
A traditional transactional application typically maps known inputs to known processing rules and expected outputs. Validation therefore focuses heavily on confirming that the configured system performs those functions consistently. Many AI systems, particularly generative and adaptive systems, introduce a different set of considerations.
1. Output differences
Many AI systems, particularly generative models, can produce variable outputs even when given identical or nearly identical inputs, depending on the model architecture and inference configuration.
A computer-vision model used for defect detection can also change confidence scores when lighting, camera angle, equipment condition, or other operating conditions differ from the data represented during development.
When an AI system supports a regulated decision, process, or record, organizations should determine which inputs, outputs, prompts, configurations, approvals, and supporting metadata must be retained to demonstrate the integrity and reconstructability of the regulated activity.
An AI pilot that performs well in testing but lacks appropriate traceability, validation evidence, and governance may be difficult to defend when it is moved into regulated use.
2. Model and performance drift
AI performance can degrade over time when real-world data, populations, equipment, suppliers, or operating conditions diverge from the conditions represented during model development. A static deployed model does not necessarily change merely because it encounters new data; rather, the relationship between its inputs and real-world outcomes may change. Adaptive systems introduce additional change-management considerations because the model itself can evolve.
In one published study evaluating 128 model-and-dataset combinations, temporal performance degradation was observed in approximately 91% of the combinations studied. That finding does not mean 91% of all production AI models will degrade, but it illustrates why lifecycle performance should not be assumed to remain constant.
FDA's January 2025 draft guidance on AI used to support regulatory decision-making for drugs and biological products recommends a risk-based credibility-assessment framework tied to a model's context of use. The document is draft, non-binding guidance, not a universal AI regulation. Appropriate monitoring and maintenance should be determined based on the model, its role, and the risk of the context of use.
For AI systems classified as high-risk under the EU AI Act, providers are required to establish a documented, risk-proportionate post-market monitoring system that evaluates performance and continuing compliance throughout the system lifecycle.
3. Version and configuration control
AI-enabled systems can have a larger configuration surface than traditional applications. Depending on the architecture, relevant controlled elements may include:
- Base model and service versions, including vendor-controlled changes
- Fine-tuning or adapter layers
- Prompt templates and system instructions
- Retrieval-augmented generation (RAG) corpora
- Embedding models
- Preprocessing and post-processing pipelines
- Model configuration and inference parameters
If an inspector asks how a regulated recommendation was generated and who approved it, the organization should be able to reconstruct the relevant system state and human oversight. If the model version, configuration, prompt, data source, or approval history cannot be established, it may be difficult to defend the basis for the regulated decision.
4. Data lineage and integrity
AI systems may incorporate internal, vendor-supplied, historical, or public datasets. Before those datasets support regulated activities, their provenance, quality, transformations, and fitness for intended use should be evaluated.
ALCOA+ provides a widely used framework for evaluating data-integrity characteristics. Regulated records should be attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring, and available.
Applying ALCOA+ principles to AI-enabled processes introduces additional challenges because regulated evidence may depend not only on human-entered records, but also on model versions, source data, prompts, automated transformations, retrieved information, and generated outputs.
Without appropriate system design, organizations may be unable to establish clear attribution among the user, model, model version, input data, generated output, and approving reviewer. Generative AI can also produce incorrect or fabricated content, making verification and appropriate human oversight important where outputs affect regulated activities.
FDA's January 2025 draft credibility guidance recommends that sponsors establish sufficient evidence of model credibility for the defined context of use, including appropriate information about model development, data, evaluation, and performance.
5. Appropriate environment segregation
Development, testing, and production activities should be appropriately segregated and controlled according to risk so that experimental AI changes cannot inadvertently affect validated production processes or regulated data.
For example, testing new prompts or retrieval configurations against live regulated data may create data-integrity, privacy, security, or change-control concerns if the environment and permissions are not appropriately governed.
The cost of GxP compliance failures
Deficiencies involving regulated systems can lead to remediation costs, delayed approvals or manufacturing changes, reputational damage, operational disruption, and enforcement exposure. The scale of impact depends on the findings, products, facilities, and business context.
1. Regulatory actions
FDA enforcement tools include Form FDA 483 observations, warning letters, recalls, seizures, injunctions, import restrictions, consent decrees, and other actions, depending on the facts and statutory authority involved.
FDA issued 303 drug- and biologic-related warning letters in FY2025, up 59% from 190 in FY2024. However, industry analysis indicates that only 135 of the FY2025 letters were inspection-based, so the increase should not be interpreted solely as a rise in manufacturing or quality-system inspection failures.
In one 2025 analysis of 3,678 FDA warning letters, 11.7% had an identified close-out letter at the time of review; among those closed, the average time to close-out was 486 days. Recently issued letters naturally had less time to reach closure, and FDA does not issue a close-out letter in every circumstance.
2. Reputational and market impact
Public FDA enforcement actions can create reputational and financial consequences, although market impact varies significantly by company, product portfolio, severity of the findings, and expected remediation cost and duration.
Guerbet provides a recent example. FDA issued an amended warning letter on October 17, 2025 concerning CGMP deficiencies at the company's Raleigh facility. In December 2025, Guerbet reduced its financial outlook as remediation work delayed batch releases; its shares fell approximately 19% following the announcement. By July 2026, Guerbet projected roughly €35 million in costs associated with the Raleigh remediation plan.
Applied Therapeutics also experienced a sharp decline in market value following FDA's November 2024 Complete Response Letter and subsequent warning letter. The warning letter cited, among other issues, deletion of electronic clinical source data and associated audit trails by a third-party vendor, as well as inadequate disclosure of dosing errors. Securities litigation followed, and a class-action settlement providing approximately $15 million in cash was later approved.
3. Project and approval delays
Unresolved compliance deficiencies can delay regulatory approvals, supplements, manufacturing changes, or other FDA actions when the deficiencies affect the relevant facility, product, study, or application.
For high-value products, even a relatively short regulatory or manufacturing delay can have material commercial consequences.
4. Financial impact
Remediation costs vary substantially depending on the scope of the deficiencies, facility complexity, product portfolio, duration of corrective actions, and whether production or distribution is interrupted.
One published analysis of the U.S. medical-device industry estimated that non-routine quality events—including recalls, warning letters, consent decrees, warranty costs, and litigation—cost the industry approximately $7.5–$9 billion annually, with an additional $1–$2 billion in estimated lost sales. That estimate should not be generalized to all pharmaceutical and biotechnology companies without qualification.
5. Operational disruption
Serious unresolved CGMP violations can escalate beyond warning letters to enforcement actions such as injunctions, seizures, consent decrees, import restrictions, or other measures that may significantly disrupt operations.
What audit-ready AI infrastructure should address
Current FDA guidance and draft guidance, applicable GxP requirements, GAMP 5 and the ISPE GAMP AI Guide, data-integrity expectations and, where applicable, the EU AI Act point toward several recurring control areas for AI-enabled regulated systems. The exact control set should be risk-based and tied to intended use.
| Control area | Typical enterprise pilot | GxP-oriented approach |
|---|---|---|
| Intended use / COU | Broad business objective | Documented intended use and context of use appropriate to the regulatory application, including relevant population/use case, inputs, operating environment, human role, and decision consequence. |
| Data governance | Data assembled primarily for experimentation | Documented provenance and transformations, quality controls, applicable bias/representativeness evaluation, and privacy/security controls appropriate to the data and jurisdiction. |
| Validation / assurance | Ad hoc testing against representative examples | Risk-based verification and validation with predefined acceptance criteria, appropriate challenge testing, and documented evidence of fitness for intended use. |
| Lifecycle monitoring | Limited post-deployment review | Risk-based performance monitoring designed to detect changes in inputs, outputs, operating conditions, or performance that could affect continued fitness for intended use. |
| Change control | Updates managed as routine IT changes | Formal, version-controlled change management. For eligible AI-enabled medical devices, an FDA-authorized PCCP may define certain planned modifications and the methods used to validate and implement them. |
| Traceability / records | Application or API logs where available | Records and metadata sufficient to reconstruct relevant regulated activities, including model/version, important configuration, relevant inputs/outputs, reviewer actions, and timestamps as appropriate to intended use. |
Intended use and context of use
For AI used to support drug and biologic regulatory decision-making, FDA's January 2025 draft guidance centers the credibility assessment on the model's context of use (COU). A useful COU description identifies the question the model addresses, how the output will be used, the relevant population or operating context, and the consequence of an incorrect result.
A material change in context of use can alter the risk and evidence needed to establish model credibility and should trigger reassessment. A model evaluated for retrospective analysis, for example, should not automatically be assumed suitable for real-time patient triage or another materially different application.
A documented risk assessment should consider both the influence of the model on the decision and the consequence of an incorrect model output. Failing to document that rationale can leave an organization without a defensible basis for the level of evidence and controls applied.
Training and evaluation data governance
FDA's January 2025 draft credibility framework recommends describing major elements of the AI model and relevant model-development data when the model is used to support regulatory decision-making.
Depending on intended use and risk, good governance may include data quality gates, documented source and transformation lineage, evaluation of representativeness and potential bias, appropriate separation of development and evaluation data, and label-quality controls when human annotation is involved.
For high-risk AI systems within the scope of the EU AI Act, the Act includes specific requirements concerning training, validation, and testing data governance. Applicable privacy requirements, including GDPR or HIPAA where relevant, should be handled according to jurisdiction and data type.
Risk-proportionate validation
Traditional validation and assurance concepts remain relevant, but AI systems may require additional evaluation because performance can depend on data, model configuration, operating context, and probabilistic behavior. Depending on system risk and decision consequence, appropriate testing may include:
- Performance metrics aligned to the intended decision
- Out-of-distribution or robustness testing
- Stress testing and adversarial-security evaluation where relevant
- Benchmarking against appropriate alternative methods or human performance
- Uncertainty characterization for higher-risk predictions
- Documented acceptance criteria and change/revalidation triggers
Continuous and periodic monitoring
The monitoring strategy for an AI-enabled GxP system should be risk-based and designed to detect changes that could affect continued fitness for intended use. Depending on the system, this may include:
- Performance dashboards that compare current results with approved baselines
- Statistical monitoring of relevant input and output distributions
- Concept- or performance-drift indicators where ground-truth outcomes are available
- Periodic review with documented evidence of continued suitability
- Defined escalation and change-control triggers when performance thresholds are crossed
Change control for AI
AI-related changes should be integrated into the organization's formal change-control process rather than treated as undocumented routine IT updates.
For certain AI-enabled medical devices, FDA's Predetermined Change Control Plan (PCCP) framework provides a mechanism for managing specified future modifications within an FDA-authorized plan. A PCCP may describe planned modifications, the methodology used to develop, validate, and implement those changes, and an assessment of their potential impact.
One published analysis found that 5.4% of AI-enabled devices authorized during 2023–2025 included an FDA-authorized PCCP, rising to 9.7% among devices authorized in Q4 2025. PCCP is a medical-device mechanism; it is not a generic FDA change-control requirement for every GxP AI system.
Audit trails, traceability, and record reconstruction
Applicable GxP requirements and guidance emphasize data integrity, traceability, and the ability to reconstruct relevant regulated activities. AI-enabled workflows may require additional metadata to preserve that traceability.
For applicable electronic records, audit-trail controls should preserve the history of relevant changes and deletions, identify significant changes, prevent unauthorized alteration, and support review and reconstruction of the regulated activity.
AI-generated information used as part of a regulated GxP activity should be assessed to determine the records and metadata needed to reconstruct the activity and demonstrate appropriate oversight. Depending on intended use, that record may include the model and version, relevant configuration, input data or prompt, generated output, post-processing, user/reviewer identity, approval status, and timestamp.
Where users can accept, modify, reject, or override AI-generated recommendations in a regulated workflow, organizations should determine whether those actions and the resulting final decision need to be captured in the audit trail based on intended use and regulatory risk. There is no universal requirement to record what an AI 'almost said.'
How Outer Edge Technology can help build audit-ready AI infrastructure
Deficiencies involving regulated AI-enabled systems can contribute to remediation costs, project delays, operational disruption, and regulatory exposure. The strongest time to address those controls is before the system is relied upon for regulated activities.
OET's approach builds on the same compliance-first infrastructure discipline used in validated environments: documented architecture, data integrity, access control, change management, traceability, supplier governance, and operational evidence.
A practical starting point:
- Inventory current AI pilots and identify which ones touch regulated data, decisions, or records.
- Define intended use, context of use, ownership, and human oversight.
- Identify gaps in data lineage, configuration/version control, access control, monitoring, supplier governance, and auditability.
- Implement controls before the system becomes operationally dependent or subject to inspection.
- Maintain evidence through the full lifecycle, not only at initial deployment.
OET AI Guardrails is designed to help organizations establish controls aligned with recognized regulatory expectations and industry frameworks. Depending on the use case, that may include:
- Data-lineage and provenance controls
- Version and configuration management
- Model and supplier governance
- Controls mapped to NIST AI RMF and ISO/IEC 42001 where appropriate
- Risk-based audit-trail and traceability design
- Validation and assurance documentation informed by GAMP 5 Appendix D11, the ISPE GAMP AI Guide, applicable GxP requirements, and relevant FDA guidance
The goal is not to bolt compliance onto an AI pilot after the fact. It is to design the evidence, accountability, and lifecycle controls into the environment from the beginning.
FAQs
What did FDA's April 2, 2026 Purolea warning letter say about AI in CGMP activities?
FDA issued a warning letter to Purolea Cosmetics Lab containing an explicit section titled 'Inappropriate Use of Artificial Intelligence in Pharmaceutical Manufacturing.' FDA found that the company had used AI to create drug-product specifications, procedures, and master production or control records without adequate Quality Unit review.
FDA stated that when Purolea used AI to support CGMP activities such as developing procedures and specifications, AI-generated outputs and recommendations needed review and clearance by an authorized human representative of the Quality Unit. FDA also emphasized that reliance on AI does not relieve a manufacturer of its existing CGMP responsibilities.
The letter is important because it shows FDA applying existing CGMP requirements to AI-assisted work. It should not, however, be read as creating a universal rule that every AI output anywhere in a GxP environment requires the same review workflow.
What is a Predetermined Change Control Plan (PCCP), and when does it matter?
A PCCP is an FDA mechanism for certain AI-enabled medical devices. It can be included in a 510(k), De Novo, or PMA submission to prospectively describe specified future modifications and the methodology for developing, validating, and implementing them.
If a future modification remains within an FDA-authorized PCCP and follows the authorized methodology, the manufacturer may be able to implement the change without a new premarket submission. Without an authorized PCCP, significant modifications must be evaluated under FDA's existing change-submission requirements; not every retraining event or model update automatically requires a new filing.
What challenges must be addressed when using cloud-based LLMs in a GxP process?
Cloud-based LLMs are not inherently incapable of supporting validated or otherwise controlled GxP use. The challenge is whether the specific service, architecture, supplier controls, and intended use provide enough evidence and control for the regulated process.
Cloud LLMs can present change-control challenges because the regulated company may not control the underlying model, infrastructure, release schedule, or all configuration changes. Supplier agreements, version transparency, change notification, monitoring, and documented impact assessment therefore become important elements of the control strategy.
Limited transparency into a proprietary model's training data can complicate supplier assessment, explainability, bias evaluation, and demonstration of fitness for certain higher-risk uses. The significance of that limitation should be evaluated based on the model's role and regulatory risk; inability to inspect every item in a commercial model's pretraining corpus does not automatically constitute an ALCOA+ violation.
Multi-tenant cloud services are not inherently incompatible with GxP. They require appropriate assessment of logical tenant isolation, access controls, encryption, data residency, privacy, cybersecurity, availability, backup, supplier controls, and other requirements relevant to the intended use.
Using a cloud provider or AI vendor does not transfer the regulated company's responsibility for compliance. Supplier responsibilities can be allocated contractually, but the regulated organization remains responsible for demonstrating that the overall process and system are suitable and controlled for their intended regulated use.