Utilizing Confidentiality, Integrity, and Availability Standards to Verify AI Authenticity
In the rapidly evolving landscape of artificial intelligence (AI), organizations operating in GxP-regulated industries must adopt new procedures or revise existing ones to ensure data accuracy, consistency, and structure for AI use. This approach balances innovation with compliance, ensuring AI systems deliver reliable, controlled, and explainable outcomes that protect product quality and patient safety.
The process begins by defining the AI system's intended use, aligning it with the specific GxP process it supports. This includes specifying functional scope, performance expectations, and regulatory context, ensuring clarity on how and where the AI system is applied.
A risk assessment is the next crucial step, considering AI-specific factors such as data quality, algorithm complexity, potential impact on product quality or patient safety, and model adaptability over time. The goal is to identify "reasonably foreseeable" failure modes and determine whether those failures would pose a high process risk.
Based on the risk assessment, a tailored validation strategy is defined. This includes validation of training and test data integrity, metrics-driven model validation against predefined performance criteria, verification of algorithm transparency and explainability, implementation of human oversight protocols, formal change control and impact assessment procedures for model updates or retraining, and continuous monitoring to detect performance drift, data or model degradation, and evolving risk profiles.
Throughout the AI system's lifecycle, comprehensive, audit-ready documentation is maintained. This includes records of intended use, validation protocols, data provenance, training and testing datasets, performance results, change history, and human review decisions, supporting full traceability and regulatory inspection readiness.
To prepare an organization for the use of AI, it's critical to assess its current operational state and compare it with future business goals, compliance requirements, and system capabilities. Organizations should engage qualified vendors early to ensure alignment with both technical requirements and regulatory expectations when using AI systems.
In the ever-evolving world of AI, organizations face uncertainty in how to validate AI systems and ensure compliance. The AI Maturity Model, developed by the ISPE D/A/CH AI Validation Group, offers a structure for assessing the intended use of AI systems. This model considers two key dimensions: Control Design and Autonomy, which help organizations tailor validation efforts to risk.
When using AI in regulated environments, sufficient objective evidence must be provided to demonstrate that the AI systems have been independently assessed. Within a Computer Software Assurance (CSA) framework, testing only what is necessary to mitigate identified risks is recommended. AI technologies require robust change management to remain in a validated state, including impact assessments, code reviews, version control, and regression testing.
To effectively integrate AI in GxP environments, the first step is to clearly define its intended use, including understanding its impact on product quality or patient safety. Clarkston, with deep expertise in the life sciences industry, life science technologies, can guide the journey to AI.
By adopting this comprehensive approach, organizations can validate AI systems used in GxP-regulated processes, ensuring they meet the unique characteristics of AI such as model training, explainability, and continuous learning, while maintaining compliance with regulatory expectations.
[1] ISPE GAMP AI Guide [2] EMA Annex 22 [3] Guidelines for AI-Enabled Validation Technologies [4] AI-Enabled Validation Technologies: Best Practices for Ensuring Data Integrity [5] Computer Software Assurance (CSA) in the Life Sciences Industry
- To meet regulatory compliance in life sciences and other GxP-regulated industries, organizations might consult Clarkston for guidance on defining the intended use of an AI system, aligning it with specific GxP processes, and ensuring regulatory context.
- Risk assessments for AI systems should consider factors like data quality, algorithm complexity, potential impact on product quality or patient safety, and model adaptability over time, to identify "reasonably foreseeable" failure modes and associated process risks.
- For AI systems in retail, consumer products, or life sciences, a tailored validation strategy should incorporate measures like data integrity validation, metrics-driven model validation, algorithm transparency verification, and human oversight protocols.
- In the lifecycle of an AI system, maintaining comprehensive, audit-ready documentation of intended use, validation protocols, data provenance, and performance results is essential for traceability and regulatory inspection readiness.
- In adopting AI, organizations should engage in change management practices, including impact assessments, code reviews, version control, and regression testing, to ensure AI technologies remain in a validated state and meet regulatory expectations, as per CSA guidelines.
