Protecting Distributed Systems: AI-Driven Forensic Tools for Cloud and Edge Computing

Abstract

In recent years, the rapid expansion of cloud and edge computing systems has introduced significant security challenges, particularly in the realm of cyber forensics. With distributed systems becoming the backbone of modern enterprises, safeguarding them from sophisticated cyber threats has become paramount. This paper explores the potential of artificial intelligence (AI)-driven forensic tools to enhance cybersecurity practices across distributed cloud and edge computing environments. By leveraging AI models such as machine learning, deep learning, and anomaly detection techniques, we aim to develop tools capable of detecting, analyzing, and mitigating cyber incidents in real-time. The research emphasizes the integration of AI into existing forensic frameworks to improve their scalability, accuracy, and response times in the face of evolving threats. We present a detailed analysis of various AI techniques, including supervised and unsupervised learning, and their application in the identification of anomalies, intrusion detection, and incident response. Our findings suggest that AI-based forensic tools are highly effective in reducing the time required for threat detection and investigation, as well as enhancing the ability to predict and prevent future cyber attacks. The study highlights the potential of these tools to transform the cybersecurity landscape, particularly in distributed systems where real-time threat mitigation is critical for maintaining system integrity.