An Integrated Cloud and Network Architecture Utilising AI and LLMs for Secure Web Applications and Financial Fraud Analysis

Abstract

The swift expansion of cloud-based online applications and digital financial services has markedly heightened the intricacy of security risks and financial crime. Conventional rule-based security solutions and standalone analytics platforms are inadequate for countering advanced cyberattacks and emerging fraud trends. This study presents a cloud and network integrated architecture utilising artificial intelligence (AI) and large language models (LLMs) to improve the security of web applications and facilitate sophisticated financial fraud analytics. The architecture integrates sophisticated extract–transform–load (ETL) pipelines, network-sensitive monitoring, AI-fueled anomaly detection, and LLM-based reasoning to provide real-time and scalable analytics. The suggested approach integrates cloud infrastructure with network telemetry and financial transaction data, facilitating comprehensive visibility, adaptive threat detection, and elucidated fraud insights. Experimental assessment and application analysis reveal superior detection accuracy, diminished response time, and augmented system resilience relative to conventional security and fraud detection methods.