Article Context:

1. Computer System Validation
2. AI Validation
3. AI Validation Framework
4. Agentic AI Platform

Implementation of AI solutions is evolving quickly from experimentation to operational deployment within the life sciences industry. Currently, AI-powered tools are deployed for pharmacovigilance review support, quality event evaluation, batch release decisions, clinical analysis, and documentation management. While the technologies present great opportunities for productivity and innovation, they also come up with an inherent issue that was previously unknown to the world of traditional validation.

Computer System Validation (CSV) was built on the assumption that once validated, a system will be stable unless altered using a formal change process. However, not all AI systems follow these principles. For example, the performance of the solution can change based on new data, environment, or user interaction.

As companies start leveraging AI technologies in regulated GxP operations, the following question arises: How can organizations ensure the stability and reliability of AI-driven decisions in the absence of software changes? This is achieved by shifting away from single validation approaches to continuous governance of the AI-powered solution.

Why AI Requires a Different Validation Strategy

Compared to traditional software systems, AI is extremely sensitive to the data fed into it. Although the software does not change, changes in the data can result in behavioral changes.

Three features that set AI apart from traditional systems:

1. Performance of the AI system may change due to differences between real-life and training data.
2. Risk to be regulated is determined by how the output of the AI system is going to be used.
3. The requirements to validate demand organizations prove that systems keep working correctly.

Such characteristics mean that simple testing is not enough, and the organization should have a systematic approach that will ensure proper functionality and traceability.

A Practical 5-layer Framework for Continuous AI Validation

Intended Use Statement & Risk Classification

Every AI project needs an intended use statement to begin with. The intended use statement should state the purpose of the project, the intended users, the decision-making process aided by the model, and the possible consequences of erroneous output. The next step is to classify the risk based on regulatory considerations, patient safety, product quality, and operational significance. An AI algorithm for document classification might need only minimal controls, but one that affects batch release will need much closer scrutiny. Risk classification sets the foundation for subsequent validation efforts.

Monitor Drift Continuously

Drift involves changes to the environment within which the AI application is operating that adversely impacts performance. It is critical for organizations to monitor three types of drift:

• Data Drift refers to differences between the production and validation data.
• Concept Drift occurs when the connection between inputs and desired outputs shifts over time.
• Performance Drift is identified when any of the measures related to accuracy, dependability, and precision start deteriorating.

Each one of these drifts should have pre-defined levels, reviews, escalation points, and owners. Without continuous detection, organizations are likely to rely on AI outputs that do not meet the level of validation anymore.

Implement Risk-Based Revalidation Decisions

A frequent misunderstanding is that any model modifications always demand revalidation. However, risk-based management in AI governance calls for pre-set criteria that would help decide on the course of action. Changes falling within accepted ranges may be simply logged and monitored.

Small deviations could require targeted testing to address affected features of the system. Substantial changes like re-training the model, architectural alterations, or large performance degradation might need further testing or even stopping the system. Well-documented decision logic will prevent confusion and inconsistency and can be useful during an audit by the regulators.

Enhance Audibility and Documentation

The importance of auditability and documentation cannot be overstated in regulated environments. Traceability should be provided for all aspects of validation, testing, risk assessment, approvals, investigations, and revalidation.

Just as critical is documenting situations when revalidation was not carried out because some action was deemed unnecessary. Evidence of decisions and supporting rationale can be crucial during regulatory audits.

Implement Effective Lifecycle Governance

Effective validation of AI will require an appropriate governance structure to address concerns regarding ownership, accountability, and escalation. Business stakeholders should govern how the technology is used and its performance. IT departments should be responsible for performance monitoring, changes, and infrastructure.

In addition, there should be governance for quality and compliance to verify continued adherence to regulatory requirements. Governance principles should be able to outline how any incidents would be handled, retirement criteria, and review processes.

Execution Modernization via Agentic AI Platform

The manual approach to continuous validation generates enormous friction. Life science companies rely on native intelligent software platforms, such as Compliance Group’s iQuality system, for long-term compliance. As part of the ISO/IEC 42001-certified AI management system, iQuality is rolled out within weeks instead of months, ensuring that compliance is built into its foundation.

Key among these approaches is the embedded agentic AI harness platform CLAiRE, which works within the Document, Validation, and Quality Excellence modules within the iQuality system. Working as an autonomous compliance co-pilot,CLAiRE ensures automation of validation documentation, resulting in 23% reduction in protocol development time, and optimizing review cycles for up to 40% reduction. Additionally, it ensures Continuous Audit Trail Reviews for 100% data integrity and triggers automatic CAPAs when drifts are observed. Through the use of iQuality and CLAiRE, the validation effort can be significantly reduced by 60%.

Conclusion

AI is revolutionizing life sciences, but the success of AI adoption relies on the preservation of trustworthiness, reliability, and regulatory compliance. A mere reliance on traditional validation methodologies will not suffice, especially when dealing with learning algorithms. Using techniques such as intended use determination, drift detection, risk-based revalidation, extensive documentation, and good governance, an organization can design an effective strategy to manage AI in GxP settings. It is now time to stop asking whether it is possible to validate AI. The real question is how to prove that the AI system still serves its intended purpose throughout the process of operation.