Integrating Next-Generation SIEM with Data Lakes and AI: Advancing Threat Detection and Response

Abstract

The article focuses on how Next-Gen SIEM can be extended with Data Lakes and AI to improve threat detection and response in current threat landscapes. Conventional SIEM tools have several major disadvantages: they could be more scalable, their false positive rates can be extremely high, and data processing takes too much time due to the constantly growing number and levels of sophistication in cyber threats. These limitations may result in delayed threat detection, alert fatigue, and operations nightmares for security operations.

Data Lakes form the center of the proposed architecture to ensure the large raw, unstructured data from different sources are integrated and analyzed in real time. When applied, the system will be able to identify anomalies, evolve with threats, and improve on false positives with the help of superior machine learning algorithms. This integration also solves most of the inherent problems of traditional SIEM and provides more general and efficient solutions for improved security postures for organizations, as this article describes how to orientate CSFs for cybersecurity and SOCs. It demonstrates how various current integrated security technologies improve the detection rates, accuracy, the burden on the security personnel and the human information defense system.