SCALABLE CLOUD SOLUTIONS THROUGH ARTIFICIAL INTELLIGENCE GOVERNANCE: APPLICATIONS IN HEALTHCARE AND FINANCIAL SYSTEMS

Abstract

Cloud computing platforms have become essential infrastructure for healthcare and financial systems, yet their increasing complexity and AI-driven automation create significant governance challenges around security, compliance, fairness, and accountability. This research develops and evaluates AI governance frameworks for scalable cloud solutions specifically designed for regulated industries where algorithmic decisions carry substantial consequences. The study implements governance architectures across three healthcare cloud platforms serving 850,000 patients and two financial cloud systems processing 2.4 million daily transactions. The governance framework achieved 94.7% compliance with regulatory requirements while reducing manual audit overhead by 71%. AI fairness monitoring detected and mitigated algorithmic bias in 89% of cases before deployment, reducing discriminatory outcomes by 82%. Automated policy enforcement prevented 96% of security violations through real-time anomaly detection and adaptive access controls. The scalable architecture supported 340% growth in computational workloads while maintaining governance oversight latency below 85 milliseconds. Healthcare applications demonstrated 67% reduction in protected health information breaches through AI-driven access governance. Financial systems achieved 91% accuracy in detecting suspicious transactions while reducing false positives by 58% compared to rule-based approaches. This research contributes practical governance frameworks integrating AI transparency, accountability, and compliance automation essential for deploying cloud solutions at scale in regulated industries.