AI-Augmented Security Operations Center¶

Production Implementation of Machine Learning-Enhanced Intrusion Detection¶

Welcome to the comprehensive documentation for the AI-SOC (AI-Augmented Security Operations Center) platform - a production-ready research implementation that validates academic findings on AI/ML integration in security operations.

Project Overview¶

The AI-SOC platform is a comprehensive implementation of an AI-Augmented Security Operations Center developed as a research platform for investigating the practical application of machine learning techniques to real-world cybersecurity operations. This project integrates enterprise-grade Security Information and Event Management (SIEM) infrastructure with advanced machine learning models to achieve automated threat detection, intelligent alert prioritization, and context-aware security analysis.

Key Achievements¶

99.28% ML Accuracy on CICIDS2017 benchmark dataset
100% Deployment Success Rate after automation
<15 Minute Deployment Time (reduced from 2-3 hours)
6 Integrated Microservices with comprehensive health monitoring
Production Validation Score: 9.5/10

Research Foundation¶

This implementation builds directly upon the academic survey paper:

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

Srinivas, S., Kirk, B., Zendejas, J., Espino, M., Boskovich, M., Bari, A., Dajani, K., & Alzahrani, N. School of Computer Science & Engineering, California State University, San Bernardino, 2025

The survey analyzed 500+ papers using PRISMA methodology, identifying 8 critical SOC tasks where AI/ML demonstrates measurable impact. Our implementation validates these findings through production deployment.

Quick Start¶

Get started with AI-SOC in under 15 minutes:

# Clone the repository
git clone https://github.com/zhadyz/AI_SOC.git
cd AI_SOC

# Windows: Double-click START-AI-SOC.bat
# Linux/macOS: ./quickstart.sh

# Access dashboard
open http://localhost:3000

View Complete Installation Guide →

Documentation Sections¶

Research Foundation ¶

Complete survey paper (Baseline.md)
Research context and methodology
Academic contributions and validation

Getting Started ¶

Quick start guide
Installation instructions
System requirements
User documentation

Architecture ¶

System architecture overview
Network topology diagrams
Component design details
Data flow analysis

Experimental Results ¶

ML model performance metrics
Baseline model comparisons
Training reports and validation
Production testing results

Deployment ¶

Deployment procedures
Docker architecture
Production deployment guide
Performance optimization

Security ¶

Security configuration guide
Baseline security settings
Hardening procedures
Incident response playbooks

API Reference ¶

ML Inference API documentation
Alert Triage API specifications
RAG Service API reference

Development ¶

Project status and roadmap
Contributing guidelines
Development workflows

For Academic Reviewers¶

This platform is designed for academic institutional review and provides:

Complete Research Traceability: Clear connection from survey findings to implementation to validation
Empirical Evidence: Production metrics validating theoretical predictions
Transparent Documentation: All challenges, solutions, and results documented
Reproducible Results: Automated deployment enables independent validation
Novel Contributions: Solutions to deployment complexity beyond survey scope

View Academic Contributions →

System Performance¶

Metric	Value
ML Classification Accuracy	99.28%
Inference Latency	2.5s average
Throughput Capacity	10,000 events/second
Deployment Success Rate	100%
Service Uptime	99%+ (validated)
Production Readiness	9.5/10

Authors and Contributors¶

Implementation Developer: Abdul Bari Survey Research Team: Srinivas, Kirk, Zendejas, Espino, Boskovich, Bari Faculty Advisors: Dr. Khalil Dajani, Dr. Nabeel Alzahrani Institution: California State University, San Bernardino

View Full Acknowledgments →

Citation¶

If you use this work in your research:

For the Survey Paper:

@article{srinivas2025aiaugmented,
  author = {Srinivas, Siddhant and Kirk, Brandon and Zendejas, Julissa and
            Espino, Michael and Boskovich, Matthew and Bari, Abdul and
            Dajani, Khalil and Alzahrani, Nabeel},
  title = {AI-Augmented SOC: A Survey of LLMs and Agents for Security Automation},
  year = {2025},
  institution = {California State University, San Bernardino}
}

For the Implementation:

@software{bari2025aisocimplementation,
  author = {Bari, Abdul},
  title = {AI-SOC: Production Implementation of AI-Augmented Security Operations},
  year = {2025},
  url = {https://github.com/zhadyz/AI_SOC}
}

View Complete Citation Guide →

Contact¶

GitHub Issues: Report bugs or request features
Email: abdul.bari8019@coyote.csusb.edu
Repository: github.com/zhadyz/AI_SOC

Repository Stats¶

Static Stats:

Development Time: 3 weeks (October 2025)
Docker Services: 6 core services
Documentation Pages: 25+
Test Coverage: 200+ test cases

License¶

Apache License 2.0 - Free for commercial and academic use.

View License Details →