AI-Driven Quality Engineering for Regulated Enterprise Systems

This paper proposes a framework for AI-driven quality engineering tailored to regulated enterprise systems in sectors like healthcare, insurance, and financial services. It argues that quality engineering must evolve beyond traditional defect detection to incorporate AI validation, risk-based testing, continuous monitoring, and automated governance controls. The framework addresses challenges unique to AI-enabled applications, including probabilistic behavior, model drift, bias propagation, and hallucinated outputs, positioning AI-driven quality engineering as essential for trustworthy enterprise AI adoption.