Validating Generative AI: A Practical Framework For Reliability And Compliance in Life Sciences

By Patrick Walsh

Generative AI offers transformative potential for automating tasks and enhancing decision-making, but its occasional inaccuracies, or “hallucinations,” highlight the critical need for rigorous validation. Computer Software Assurance (CSA) is emerging as a practical framework to validate AI systems by focusing efforts based on risk, reducing unnecessary documentation while ensuring quality and compliance. CSA emphasizes automation, data integrity, agility, and collaboration, supporting continuous verification to maintain AI reliability amid evolving data and use cases.

Validating generative AI involves both technical metrics and human review to ensure outputs are accurate, relevant, and ethical. However, challenges remain, including AI’s data quality issues, scalability of validation efforts, and rapidly changing environments, especially in regulated fields like life sciences. Regulatory bodies like the FDA advocate for a risk-based, ongoing validation approach aligned with CSA principles, encouraging early dialogue between developers and regulators to maintain AI trustworthiness over time. Real-world successes in life sciences illustrate AI’s benefits in predictive maintenance, process optimization, quality monitoring, and regulatory compliance. With structured CSA-driven validation and expert guidance, organizations can harness generative AI’s advantages safely and effectively, ensuring accuracy, compliance, and operational excellence.