AI-DRIVEN DATA GOVERNANCE AND SECURITY IN REAL-TIME CLOUD INTEGRATION

Abstract

This comprehensive article examines the transformation of data governance and security through AI-driven systems in cloud environments. The article analyzes how organizations process massive volumes of data daily, achieving near-perfect accuracy in anomaly detection while maintaining minimal latency for quality checks. Through detailed case studies of financial and healthcare implementations, the article demonstrates how AI-driven frameworks significantly reduce security incidents while substantially improving compliance monitoring accuracy to near-perfect levels. The article explores advanced encryption mechanisms processing millions of operations per second, behavioral analysis systems achieving exceptional accuracy, and automated policy enforcement handling substantial transaction volumes daily. Implementation challenges and solutions are examined, including infrastructure requirements of high-performance processors and extensive RAM configurations, alongside integration processes achieving near-complete automation. Results indicate a substantial reduction in mean time to respond (MTTR) and marked improvement in data governance efficiency through AI automation, while future directions show promise in quantum-resistant encryption and edge computing optimization. The research highlights how modern AI-driven systems have revolutionized security frameworks, enabling organizations to process unprecedented data volumes while maintaining superior security standards and operational efficiency.