AI-Driven Big Data Analytics for Transforming Cybersecurity for Zero-Day Vulnerabilities in E-Commerce Supply Chains

Abstract

The rapid expansion of e-commerce and its reliance on intricate supply chains have amplified the need for robust cybersecurity measures. The emergence of zero-day vulnerabilities—unanticipated weaknesses exploited before patches are developed—poses a significant threat to e-commerce systems. These vulnerabilities can disrupt operations, compromise sensitive customer data, and damage trust. Traditional cybersecurity approaches struggle to address the dynamic and complex nature of these threats. However, advancements in artificial intelligence (AI) and big data analytics offer transformative potential. By leveraging vast datasets, AI-driven analytics can detect anomalous patterns, predict potential vulnerabilities, and respond in real time. This paper explores how AI-powered big data analytics is reshaping cybersecurity strategies for mitigating zero-day vulnerabilities in e-commerce supply chains. It discusses the challenges inherent to securing supply chains, highlights the role of AI in predictive analytics and anomaly detection, and presents practical insights into the application of these technologies. The integration of machine learning algorithms, neural networks, and natural language processing is examined, alongside their effectiveness in uncovering hidden attack vectors. The paper also investigates how AI can adapt to evolving threats, enhance risk assessment frameworks, and foster collaboration across e-commerce stakeholders. Finally, ethical considerations and future research directions are proposed to ensure that the adoption of AI in cybersecurity remains secure, equitable, and scalable.