The Impact of Machine Learning on Incident Response Strategies

Abstract

The increasing complexity and frequency of cyberattacks have necessitated a reevaluation of traditional incident response strategies. This research paper explores the transformative impact of machine learning (ML) on incident response within cybersecurity. By integrating ML techniques, organizations can significantly enhance their ability to detect, analyze, and respond to security incidents more efficiently and effectively. This paper discusses the key roles of supervised and unsupervised learning algorithms in threat detection and anomaly identification, highlighting their capabilities in automating incident classification and prioritization. Through the examination of case studies and real-world applications, we demonstrate how ML-driven approaches facilitate rapid decision-making and improve overall response times. Additionally, we address the challenges and limitations of implementing ML in incident response, including data quality, model interpretability, and the need for continuous training. The findings underscore the importance of adopting ML-enhanced incident response strategies to proactively address the evolving threat landscape, ultimately contributing to a more resilient cybersecurity posture.