Integrating GenAI Testing into Your System Compliance and Quality Assurance Programs

Introduction:

The early adoption of GenAI technology has been demonstrated to deliver significant value while disrupting conventional approaches to product development, testing, and approval processes. As GenAI increasingly becomes integrated into applications, there arises a need for a thorough reassessment of how testing and validation procedures for AI embedded systems should be conducted.

In this article, we delve into the perspectives of two esteemed members of KatalX: Head of Security and Systems Compliance, Mikhail Kostyukovski, and Head of QA and Automation, Martin Zakrzewski.

.

Head of Security and Systems Compliance, Mikhail Kostyukovski:

In the realm of Supply Chain Management for Life Sciences, ensuring system compliance entails adherence to regulatory standards and industry best practices. Given that our Advanced Visibility and Collaboration software is designed for global use, it is imperative that our IT systems conform to the stringent requirements set forth by regulatory bodies such as the FDA in the US and the EMA in the EU. Furthermore, compliance extends to common regulations like GDP and GMP for product manufacturing and distribution, as well as regulations governing data privacy and protection. With the increasing integration of AI into our KatalX Platform, collaboration with the Chief Product Officer and Head of Quality Assurance is paramount to ensure that AI models align with regulations and standards and undergo rigorous testing and continuous revalidation. Alongside cybersecurity considerations, the integration of GenAI necessitates the establishment of processes to ensure the secure generation of new data for the application and the implementation of continuous validation procedures.”

.

Head of QA and Automation, Martin Zakrzewski:

“Upon joining KatalX, my primary responsibility was to conduct application testing and ensure the establishment of robust processes for managing test results and associated reports. However, with the incorporation of AI elements, my role has expanded to encompass collaboration with the Head of Systems Compliance, Data Scientists, and the Chief Product Officer. Presently, our QA activities are focused on three major areas, necessitating collaboration with respective representatives from each domain:

1. Testing for security vulnerabilities and maintaining auditable records.

2. Testing for the expected output of AI models and maintaining records for continuous AI model validation.

3. Testing for expected behavior according to specifications approved by the Product Owner.

As our technology stack evolves and AI models are integrated into our applications, the role of QA is evolving into a multidisciplinary function akin to traditional Architect roles. This evolution requires expertise spanning programming, security, data science, cloud technologies, and beyond.”

Conclusion:

The integration of GenAI into system compliance and quality assurance programs represents a paradigm shift in the approach to testing and validation within the life sciences supply chain management domain. Through collaborative efforts and a commitment to adhering to regulatory standards and industry best practices, organizations like KatalX can leverage the transformative potential of GenAI while ensuring the safety, efficacy, and compliance of their products and systems. 

Tom Z.,