Towards an AI-Driven Automated Cybersecurity Incident Response System

Abstract

In the rapidly evolving landscape of cybersecurity, the increasing frequency and sophistication of cyber threats necessitate a paradigm shift in incident response strategies. This paper proposes an AI-driven automated cybersecurity incident response system designed to enhance the speed and efficiency of threat detection and mitigation processes. By leveraging advanced machine learning algorithms and real-time data analytics, the proposed system aims to minimize human intervention while maximizing the accuracy of threat identification and response actions. The framework integrates various AI techniques, including anomaly detection, natural language processing, and predictive analytics, to create a comprehensive solution that adapts to the dynamic nature of cyber threats. A series of experiments are conducted to evaluate the system's performance, demonstrating significant improvements in response times and accuracy compared to traditional incident response methodologies. This research underscores the critical role of AI in transforming cybersecurity practices, paving the way for more resilient and proactive defense mechanisms in the face of ever-evolving cyber threats.