Authors & Acknowledgments¶

Research Foundation¶

AI-Augmented SOC: A Survey of LLMs and Agents for Security Automation¶

Authors:

Siddhant Srinivas – siddhant.srinivas@coyote.csusb.edu
Brandon Kirk – brandon.kirk@coyote.csusb.edu
Julissa Zendejas – julissa.zendejas@coyote.csusb.edu
Michael Espino – michael.espino@coyote.csusb.edu
Matthew Boskovich – matthew.boskovich@coyote.csusb.edu
Abdul Bari – abdul.bari8019@coyote.csusb.edu

Faculty Advisors:

Dr. Khalil Dajani – khalil.dajani@csusb.edu
Dr. Nabeel Alzahrani – nabeel.alzahrani@csusb.edu

Institution:

School of Computer Science & Engineering California State University, San Bernardino

Implementation Developer¶

Abdul Bari Graduate Student, Computer Science California State University, San Bernardino

abdul.bari8019@coyote.csusb.edu • GitHub

Acknowledgments¶

This implementation builds directly upon the foundational survey paper "AI-Augmented SOC: A Survey of LLMs and Agents for Security Automation" authored by the research team listed above.

The survey's systematic literature review (500+ papers using PRISMA methodology) provided the theoretical framework and research questions that guided this implementation.

The production codebase, deployment automation, ML model training, and system architecture were developed by Abdul Bari as a practical validation of the survey's findings.

Open Source Acknowledgments¶

Wazuh Project - Comprehensive SIEM platform
Scikit-learn - Production-grade ML tools
FastAPI - Modern Python web framework
Docker - Containerization platform
ChromaDB - AI-native vector database