AI-Powered Incident Response: Automating Threat Detection and Mitigation

Abstract

The escalating frequency and sophistication of cyberattacks have exposed the limitations of traditional, human-driven incident response mechanisms, which are often too slow, resource-intensive, and error-prone to counter modern threats effectively. This paper explores the transformative role of Artificial Intelligence (AI) in automating threat detection and mitigation within cybersecurity incident response workflows. It examines how machine learning algorithms, behavioral analytics, natural language processing, and autonomous response systems are being deployed to identify anomalies, classify threats, prioritize alerts, and execute containment measures in real time. The paper further discusses the architectural components of AI-driven incident response platforms, the benefits they offer in terms of speed, scalability, and accuracy, and the challenges associated with their adoption, including false positives, adversarial manipulation, data privacy concerns, and the need for human oversight. By synthesizing current developments and emerging trends, this paper argues that AI-powered incident response represents not merely an incremental improvement but a fundamental paradigm shift in how organizations defend against cyber threats. It concludes by outlining future directions, including explainable AI, federated learning, and human-AI collaboration, that will shape the next generation of intelligent security operations.