Abstract

Artificial intelligence (AI), large language models (LLMs), and cloud computing have emerged as foundational technologies for enhancing cybersecurity, detecting anomalous behaviors, and securing data pipelines in modern web applications. Cloud architectures integrated with AI and LLM capabilities offer dynamic, scalable, and adaptive solutions for fraud detection and prevention—addressing increasingly sophisticated threats encountered by digital systems. This paper investigates how AI and LLM-driven methodologies can be embedded into cloud infrastructure to create fraud-resilient web applications with secure Extract, Transform, Load (ETL) processes. We explore system designs that leverage distributed computing, real-time inference, and contextual language understanding to improve accuracy and responsiveness in fraud detection. The study systematically reviews relevant literature from foundational cloud computing frameworks, AI-based fraud detection models, and secure data processing techniques. A research methodology comprising architectural design, implementation strategies, and evaluation metrics for performance, security, and scalability is detailed. The paper discusses key advantages, including enhanced threat intelligence, automation, and adaptability, alongside potential disadvantages, such as complexity, cost, and ethical challenges. Results and discussion focus on empirical outcomes, comparing model efficacy and cloud performance. The conclusion synthesizes findings, and future work outlines promising directions for research and deployment.