Turkish Journal of Computer and Mathematics Education
Journal license

Journal

Turkish Journal of Computer and Mathematics Education


Volume
& Issue

Volume 14, Issue 3


Published
on


Pages

1309-1314


DOI

Article

The Future of SIEM in a Machine Learning-Driven Cybersecurity Landscape


Authors

Srinivas Reddy Pulyala* Affiliation:
Splunk Engineer, Ally Financials, Troy, USA


Abstract

As cyber threats become increasingly sophisticated and complex, traditional Security Information and Event Management (SIEM) systems are struggling to keep up. The integration of artificial intelligence (AI) and machine learning (ML) into SIEM tools is transforming the way organizations detect, investigate, and respond to security incidents. This paper explores the future of SIEM tools in the context of the evolving cybersecurity landscape and discusses how organizations can prepare for the adoption of ML-enabled SIEM systems. ML-enabled SIEM systems significantly enhance the capabilities of traditional SIEM tools, enabling them to more effectively detect and respond to both known and emerging threats. Organizations must develop a robust data strategy, invest in talent, and adopt ML-enabled SIEM solutions gradually to fully take advantage of the potential of these technologies. Staying up-to-date with the latest trends in ML and cybersecurity is also crucial for organizations to maximize the benefits of ML-enabled SIEM tools.


Keywords

SIEM (Security Information and Event Management), AI (Artificial Intelligence), ML (Machine Learning), Cybersecurity, Threat Detection, and Incident Response


Citation

Pulyala, S. R. (2023). The future of SIEM in a machine learning-driven cybersecurity landscape. Turkish Journal of Computer and Mathematics Education, 14(3), 1309–1314.

Published by: Engineering Journals

Engineering Journals Logo