Design and Implementation of a Secure Cloud and Network Framework for AI-Based Predictive Analytics in Healthcare and Finance

Abstract

The rapid adoption of artificial intelligence (AI) in healthcare and financial systems has intensified the need for secure, scalable, and compliant cloud-based architectures capable of processing sensitive and high-value data. This paper presents the design of a secure cloud and network architecture tailored for AI-based healthcare and financial data processing. The proposed architecture integrates multi-layer network security mechanisms, including zero-trust networking, secure access control, encryption-at-rest and in-transit, and AI-aware data governance to ensure confidentiality, integrity, and availability of critical data. Cloud-native services are leveraged to enable scalable AI workloads while maintaining regulatory compliance with healthcare and financial standards. The framework supports secure multiparty data exchange across healthcare providers, financial systems, and enterprise platforms, enabling interoperable and real-time analytics without compromising privacy. AI-driven monitoring and anomaly detection are incorporated to enhance threat intelligence and proactive risk mitigation. The proposed architecture demonstrates how secure cloud computing and network design can effectively support AI-enabled healthcare and financial applications while addressing emerging cybersecurity challenges and data protection requirements.