AI-Powered Strategies for Cyber Incident Investigations in Scalable Edge Systems

Abstract

With the rapid expansion of edge computing systems, the need for robust, scalable, and efficient cyber incident investigation mechanisms has become increasingly critical. Edge environments, characterized by their distributed nature and real-time processing capabilities, present unique challenges in terms of security monitoring, data integrity, and incident response. Artificial Intelligence (AI), with its capacity to analyze vast amounts of data, detect anomalies, and generate insights in real time, offers an effective solution to these challenges. This paper explores AI-powered strategies for enhancing cyber incident investigations within scalable edge systems. By integrating advanced machine learning algorithms, anomaly detection models, and automated forensic tools, AI can significantly improve the speed, accuracy, and scalability of cyber incident detection and response in edge environments. The paper reviews current methodologies, identifies key challenges, and presents a framework for AI-driven incident response in distributed edge systems. Through practical case studies, the paper demonstrates the potential of AI to provide actionable intelligence, reduce response times, and mitigate the impact of cyber threats in real-world edge environments.