SOC AI Omega
AI + CybersecurityAI-assisted SOC monitoring platform achieving 99% threat detection accuracy.
The Problem
Traditional Security Operations Center (SOC) workflows suffer from manual threat analysis overload, leading to high triage times and critical detection delays.
System Architecture
Packet Monitoring -> ML Classification -> Threat Scoring -> Wazuh Integration -> MITRE ATT&CK Mapping.
+-------------------+ +----------------------+ +----------------------+
| Packet Monitoring | ---> | Random Forest ML | ---> | Severity Telemetry |
| (Raw Interface) | | Classification (99%) | | Alert Triage Engine |
+-------------------+ +----------------------+ +----------------------+
|
v
+-------------------+ +----------------------+ +----------------------+
| Dockerized Agent | <--- | Wazuh Agent API | <--- | MITRE ATT&CK / Sigma |
| Secure Isolation | | Automation Triggers | | Rules Classification |
+-------------------+ +----------------------+ +----------------------+Core Implementation
Designed and implemented a modular classification pipeline utilizing a Scikit-Learn Random Forest model. Telemetry alerts parse dynamically through Sigma Rules and map directly to appropriate MITRE ATT&CK tactics inside a FastAPI backend. Configured automatic Dockerized containment triggers upon high-criticality alert thresholds.
Security Considerations
Enforced off-grid local deployment to protect sensitive corporate telemetry from cloud-based leaks. Constructed strict packet signature validation checks on all incoming sniffed packets to prevent buffer overflow attacks on the parsing engine.
Quantifiable Impact
Transformed passive security monitoring into an active containment ecosystem, reducing response latency with 99.2% classification accuracy.