Enhancing Edge Computing Security with AI-Driven Threat Detection and Preventative Measures

Abstract

Edge computing has emerged as a critical paradigm in modern distributed computing architectures, bringing computational capabilities closer to data sources and end-users. While this approach offers significant advantages in terms of reduced latency, bandwidth conservation, and enhanced real-time processing capabilities, it simultaneously introduces unique security challenges that traditional security frameworks are ill-equipped to address. This research article examines the integration of artificial intelligence techniques into edge computing security frameworks, with a specific focus on threat detection mechanisms and preventative security measures. Through comprehensive analysis of existing security vulnerabilities in edge computing environments, evaluation of current AI-driven security solutions, and exploration of emerging technologies, this research provides a holistic assessment of the potential for AI to transform edge security paradigms. The article proposes a multi-layered security framework that leverages machine learning algorithms, anomaly detection systems, and automated response mechanisms to fortify edge computing deployments against evolving threat landscapes. Case studies across various industry implementations demonstrate the practical efficacy of the proposed approaches, offering valuable insights for organizations seeking to enhance their edge computing security posture in an increasingly complex digital ecosystem.