International Journal of Applied Mathematics, Sciences, and Technology for National Defense
Vol. 3 No. 3 (2025): International Journal of Applied Mathematics, Sciences, and Technology for Nati

Next-Gen SOC: Leveraging generative AI for scalable threat detection and AI-powered alert classification

Kotilingala, Sudheer (Unknown)



Article Info

Publish Date
30 Dec 2025

Abstract

The volume of alerts produced by the SIEM system causes SOC analysts to experience alert fatigue, with actual security incidents obscured by more than fifty percent of notifications being considered false positives. This inefficiency causes delays in response times and puts organisations at risk due to insufficient resource allocation. We have, therefore, introduced a new framework in this paper, which incorporates LLMs into SOC initiatives. Overall, with the help of contextual understanding elements of LLMs, our framework concludes with 95,5% accuracy to classify the alerts as false positives or actual threats. The study’s results, therefore, validate less alert fatigue, improved systems functioning, and shorter time to critical security events using the proposed methodology. As a result, this paper outlines the proposed system’s description, development, and evaluation to determine its potential for future SOC operations.

Copyrights © 2025






Journal Info

Abbrev

JAS-ND

Publisher

Subject

Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Mathematics Physics

Description

International Journal of Applied Mathematics, Sciences, and Technology for National Defense (App.Sci.Def) [e-ISSN: 2985-9352, p_ISSN: 2986-0776] is a journal published by the Foundation of Advanced Education. International Journal of Applied Mathematics, Sciences and Technology for National Defense ...