AI-Driven Cloud Architecture for Healthcare Data Governance with Financial and Risk Integration

Abstract

The growing convergence of artificial intelligence (AI), cloud computing, healthcare information systems, and financial platforms has intensified the need for robust data governance frameworks capable of managing sensitive and high-risk data. This paper presents an AI-driven cloud architecture designed to support healthcare data governance with integrated financial and risk management capabilities. The proposed architecture leverages cloud-native services to enable scalable data processing while enforcing governance policies related to data privacy, access control, and regulatory compliance. AI techniques are employed to automate data classification, policy enforcement, and risk assessment across heterogeneous healthcare and financial datasets. Secure network design, encryption mechanisms, and API-based interoperability are incorporated to facilitate controlled data exchange among stakeholders without compromising confidentiality. The architecture also integrates risk analytics to identify operational, financial, and cybersecurity threats in real time. The proposed solution demonstrates how AI-driven cloud architectures can enhance governance, transparency, and trust in healthcare data ecosystems that increasingly interact with financial systems.