Secure AI-Enabled Cloud Platforms for Healthcare Image Analysis and Financial Fraud Detection across Web Applications and 5G Networks

Abstract

The convergence of Artificial Intelligence (AI), cloud computing, web applications, and 5G network technologies has ushered in a new era of intelligent digital services, particularly in mission-critical sectors such as healthcare and financial services. This study focuses on secure AI-enabled cloud platforms designed to support healthcare image analysis and financial fraud detection across web-based applications and high-speed 5G networks. Healthcare image analysis involves processing large volumes of sensitive medical data, including X-rays, MRIs, and CT scans, with AI models that can detect and classify medical anomalies. Financial fraud detection requires real-time analysis of transactional data to quickly identify suspicious activities and minimize economic loss. Integrating these AI services into cloud architectures facilitates scalable computing, improved performance, and rapid deployment through web applications accessible across diverse devices. The advent of 5G technology enhances data transfer speeds and reduces latency, enabling real-time decision-making and remote diagnostics. However, the combination of web exposure and distributed cloud services introduces significant cybersecurity and privacy challenges. This paper proposes a secure AI-cloud framework integrating advanced encryption, authentication, privacy-preserving machine learning techniques, and adaptive risk controls to ensure the confidentiality, integrity, and availability of data and services. The model demonstrates enhanced security, system responsiveness, and operational resilience for next-generation digital services.