ADAPTIVE DATA QUALITY ASSURANCE USING GENERATIVE AI IN MULTI-CLOUD ENVIRONMENTS

Abstract

This study explores the role of Generative AI in adaptive data quality assurance within multi-cloud environments, emphasizing three core themes: adaptive intelligence, cross-cloud harmonization, and ethical governance. Using a secondary qualitative analysis of scholarly literature, the research identifies significant recoveries AI adaptability reduces manual corrections by 40% and enhances interoperability. The findings highlight that integrating Generative AI transforms static data pipelines into self-learning systems. Despite ethical and computational challenges, AI-driven frameworks establish a reliable, automated foundation for maintaining high-quality data standards in complex cloud ecosystems.