Beyond Computer System Validation: Why AI in GxP Needs an Industry Trust Framework

Artificial intelligence is rapidly becoming embedded across the life sciences value chain—from clinical development and pharmacovigilance to manufacturing, quality, and regulatory affairs. Yet as adoption accelerates, one fundamental question remains unresolved:

How do we validate AI systems for use in GxP environments?

For decades, the pharmaceutical industry has relied on Computer System Validation (CSV) to demonstrate that computerized systems consistently perform as intended. The CSV paradigm was built around deterministic software: systems whose behavior is fixed, predictable, and fully specified through documented requirements, design specifications, and test scripts.

Modern AI models are fundamentally different.

Large language models and other foundation models are probabilistic rather than deterministic. Their outputs are influenced by context, prompts, model updates, and statistical inference rather than predefined logic. While they can be tested, monitored, and benchmarked, they cannot realistically be validated using the same methodology that has served traditional software for decades.

Attempting to force AI into a conventional CSV framework creates an uncomfortable reality. Organizations either generate enormous volumes of documentation that provide little additional assurance, or they accept uncertainty while developing bespoke validation approaches that differ from one company to the next.

While the FDA and industry bodies (like ISPE GAMP) have spent the last few years actively promoting Computer Software Assurance (CSA)—a risk-based framework designed specifically to replace rigid CSV documentation with critical thinking and unscripted testing. The FDA’s CSA framework is a welcome shift toward risk-based critical thinking, but even CSA struggles with the non-deterministic, generative nature of modern foundation models.

Other industries have already solved similar problems

Interestingly, this is not the first time that an industry has faced the challenge of evaluating products whose underlying processes are too complex to assess individually every time they are used.

The food industry provides a useful analogy.

Consumers rarely inspect every manufacturing process behind a food product. Instead, they rely on trusted standards and certification schemes. Labels such as organic certification, Fairtrade, Protected Designation of Origin, or internationally recognized food safety standards provide confidence that agreed requirements have been independently assessed.

These certifications benefit everyone.

Consumers gain confidence that products meet defined standards. Manufacturers avoid explaining their production processes to every customer individually. Regulators can focus on auditing recognized frameworks rather than repeatedly assessing identical processes across multiple organizations.

The same principle applies in many other industries through ISO standards, electrical safety certifications, aviation approvals, and cybersecurity certifications.

Rather than validating every product from first principles, industries establish common standards that create trust.

Today’s AI validation burden is highly repetitive

In contrast, the current approach to AI validation in regulated life sciences places the burden almost entirely on individual companies.

Every organization adopting an AI solution is effectively required to perform its own due diligence:

Reviewing technical documentation
Assessing model limitations
Designing benchmark datasets
Executing performance testing
Evaluating risks
Documenting intended use
Preparing inspection-ready evidence
Developing governance documentation

These activities consume significant time and resources.

More importantly, they are being repeated independently across dozens—if not hundreds—of pharmaceutical companies, often evaluating the same models using remarkably similar methodologies.

The result is duplication rather than additional assurance.

Inspection readiness creates a similar challenge. Each company develops its own evidence package, prepares its own responses to regulatory questions, and constructs its own governance framework despite addressing largely identical technological risks.

Even benchmarking suffers from the same inefficiency. Multiple organizations spend months developing internal evaluation datasets for summarization, medical writing, coding, literature review, signal detection, or quality management. While slight variations exist, much of this work could be shared across the industry without compromising competitive advantage.

From model validation to task validation

Perhaps the more important shift is conceptual.

Rather than asking whether a foundation model is «validated,» the industry should ask whether it has been demonstrated to perform adequately for a specific regulated task.

The same underlying model may perform exceptionally well for document summarization while being unsuitable for regulatory decision-making or clinical causality assessment.

Validation therefore becomes task-specific rather than model-specific.

This naturally lends itself to standardized benchmarking.

Instead of every organization independently creating benchmarks for common GxP activities, the industry could collaborate on validated benchmark suites covering representative tasks such as:

Medical document summarization
Individual case safety report drafting
MedDRA coding assistance
Literature screening
Quality management documentation
Regulatory correspondence
Signal detection support
Clinical trial document generation

Vendors could demonstrate performance against these agreed benchmark sets, while individual companies would retain responsibility for validating the intended use within their own operating environment.

Towards an AI Validation Consortium

This raises an obvious question:

What if the life sciences industry developed a shared AI validation framework?

Such a framework would not replace individual company responsibilities or regulatory oversight. Rather, it would establish a common foundation upon which organisations could build their own risk-based validation activities.

An industry consortium could:

Define standard benchmark datasets for common GxP tasks.
Establish agreed performance metrics.
Develop standard documentation templates.
Publish governance expectations.
Maintain benchmark repositories as models evolve.
Facilitate independent third-party testing.
Share emerging best practices.
Engage regulators in defining acceptable evidence.

Participation would remain voluntary, but the benefits could be substantial.

Vendors would gain a clear understanding of regulatory expectations.

Pharmaceutical companies would reduce duplicated validation effort while improving consistency.

Regulators would encounter more standardized evidence during inspections.

Patients would ultimately benefit from faster and more consistent adoption of trustworthy AI technologies.

To overcome patient privacy and corporate IP barriers, these shared benchmark suites could leverage synthetic GxP datasets, anonymized reference sets, or federated evaluation methods—enforcing rigor without exposing sensitive trial data.

A new trust model for regulated AI

Artificial intelligence represents one of the most transformative technologies the pharmaceutical industry has encountered.

Yet its successful adoption will depend not only on technical capability, but on trust.

Trust cannot be generated through thousands of pages of duplicated validation documentation. It must be built through transparent governance, shared standards, independent benchmarking, and continuous oversight.

Just as internationally recognized certification frameworks transformed confidence in food safety, manufacturing quality, and countless other industries, the life sciences community now has an opportunity to create an equivalent trust framework for AI.

Rather than asking every company to reinvent AI validation independently, perhaps it is time to build the shared infrastructure that allows the industry to validate once, benchmark continuously, govern collectively, and innovate responsibly.

The technology is moving quickly.

Our validation paradigms should evolve just as fast.