A Trustworthy and Scalable AI Infrastructure for Large-Scale E-Commerce Systems

Abstract

Large-scale e-commerce platforms generate vast amounts of data that demand reliable, real-time analytics and intelligent decision-making. We propose a trustworthy and scalable AI infrastructure that integrates big data pipelines, machine learning models, and large language models (LLMs) within a secure data management framework to enhance operational efficiency and data integrity in e-commerce. The system architecture combines a distributed streaming data pipeline with robust data validation, state-of-the-art ML algorithms for prediction and anomaly detection, and LLM-driven services for unstructured data analysis, all under strict access control and privacy safeguards. Open-source e-commerce datasets were used to simulate high-volume transactions and user interactions. Experimental results under heavy load (up to tens of thousands of events per second) demonstrate that the proposed architecture maintains reliable performance and near-perfect data integrity (>98% valid data) even as throughput increases. Additionally, sensitive customer information is protected through encryption and differential privacy without significant impact on latency. The outcomes show that our architecture can uphold consistency, privacy, and trustworthy AI behavior at scale. This work provides a practical foundation for deploying AI-driven services in e-commerce settings that require both scalability and trustworthiness, bridging the gap between big data processing and responsible AI deployment.