Abstract

Artificial intelligence (AI) and large language models (LLMs) are transforming the landscape of secure web applications by empowering intelligent fraud detection and robust data processing. In cloud-native environments, fraud patterns evolve rapidly, and traditional rule-based defenses are often ineffective. AI-enabled architectures leverage scalable cloud infrastructures to support real-time analytics, anomaly detection, and secure extract-transform-load (ETL) pipelines that protect sensitive user data during ingestion and processing. LLM augmentation further enhances cybersecurity by enabling contextual reasoning, natural language understanding, and automated incident classification. This paper examines the design principles of AI and LLM-enabled cloud systems for fraud resilience, integrating advanced machine learning (ML) models with secure ETL and data governance frameworks. We highlight key components including microservices, stream processing, and zero-trust security models that together promote operational efficiency and threat adaptability. Results from existing literature demonstrate improvements in detection accuracy, reduced false positives, and enhanced compliance with data security standards. Challenges such as model explainability, data privacy, and dynamic threat adaptation are discussed. Finally, future directions focus on federated learning, generative adversarial defense techniques, and ethical AI adoption in critical financial and e-commerce platforms.