Authors - Anudeep Arora, Minal Maheshwari, Abha Pandey, Neha Chabra, Prashant Vats, Surbhi Sharma Abstract - The rapid expansion of e-commerce platforms has intensified exposure to sophisticated digital threats, including deepfake-driven identity manipulation, financial fraud, and large-scale automated attacks that undermine consumer trust. Traditional rule-based and signature-driven security mechanisms are increasingly inadequate against adaptive and AI-generated adversarial behaviors. This paper investigates the role of artificial intelligence in enabling proactive threat detection and sustained trust preservation within modern e-commerce ecosystems. We present an AI-enabled security framework that integrates deep learning-based anomaly detection, behavioral analytics, and multimodal content verifi cation to identify fraudulent transactions, synthetic media attacks, and coordinated threat patterns in real time. The proposed approach leverages temporal user behavior modeling, transaction graph analysis, and fea ture-level risk aggregation to enhance detection accuracy while minimiz ing false positives. Additionally, explainable AI components are incor porated to support transparency and regulatory compliance, thereby re inforcing user confidence and platform accountability.
