AI SOC Agent MCP Server
Enables SOC analysts to analyze security incidents, map to MITRE ATT&CK, calculate severity, and recommend remediation actions.
README
Agentic AI Security Operations Platform
An Agentic AI-powered Security Operations Platform that automates security investigations through a coordinated multi-agent workflow. The platform combines threat detection, threat intelligence enrichment, MITRE ATT&CK mapping, case management, FastAPI services, and a React dashboard to help analysts investigate and prioritize security incidents.
The project demonstrates practical implementation of Agentic AI architectures, Model Context Protocol (MCP), Pydantic AI, multi-agent systems, and security automation within a modern Security Operations Center (SOC) environment.
Key Features
Agentic AI Investigation Workflow
Security incidents are processed through a coordinated multi-agent pipeline where each agent performs a specialized security function before passing context to the next stage.
Agents include:
- Log Collection Agent
- Detection Agent
- MITRE ATT&CK Agent
- Threat Intelligence Agent
- Correlation Agent
- Severity Escalation Agent
- Case Management Agent
- Investigation Agent
The workflow produces structured investigation outputs containing threat intelligence enrichment, MITRE ATT&CK mappings, escalation decisions, and analyst recommendations.
Threat Intelligence Enrichment
- IP Reputation Analysis
- Geographic Attribution
- Threat Scoring
- Risk Prioritization
- Security Context Enrichment
MITRE ATT&CK Integration
The platform maps security events to MITRE ATT&CK techniques and tactics, helping analysts understand attacker behavior and investigation priorities.
| Attack Type | MITRE ATT&CK Technique |
|---|---|
| SQL Injection | T1190 – Exploit Public-Facing Application |
| Brute Force | T1110 – Brute Force |
| XSS | T1059 – Command and Scripting Interpreter |
Case Management
- Case Creation
- Case Search
- Analyst Notes
- Investigation Updates
- Escalation Decisions
- Severity Tracking
- Investigation History
Security Monitoring Dashboard
- Security Overview Metrics
- Incident Tracking
- AI Escalated Cases
- Threat Intelligence Panel
- MITRE ATT&CK Context
- Executive Dashboard
- Threat Hunting Workspace
- Interactive Investigation Portal
Architecture
flowchart TD
A[Windows Logs] --> G[Log Collection]
B[Linux Logs] --> G
C[AWS Logs] --> G
D[Azure Logs] --> G
E[Firewall Logs] --> G
F[Application Logs] --> G
G --> H[Detection Engine]
H --> I[Threat Intelligence]
I --> J[MITRE ATT&CK Mapping]
J --> K[Multi-Agent Workflow]
K --> L[Case Management]
L --> M[FastAPI Services]
M --> N[React Dashboard]
Multi-Agent Investigation Workflow
flowchart TD
A[Security Event] --> B[Log Collection Agent]
B --> C[Detection Agent]
C --> D[MITRE ATT&CK Agent]
D --> E[Threat Intelligence Agent]
E --> F[Correlation Agent]
F --> G[Severity Escalation Agent]
G --> H[Case Management Agent]
H --> I[Investigation Agent]
I --> J[Final Investigation Report]
Security Capabilities
Threat Detection
- SQL Injection Detection
- Brute Force Detection
- Cross-Site Scripting (XSS) Detection
- API Abuse Detection
- Session Hijacking Detection
- Correlated Multi-Vector Attacks
Investigation & Response
- Incident Correlation
- Threat Prioritization
- Severity Escalation
- Investigation Tracking
- Analyst Notes
- Executive Reporting
AI Technologies
Agentic AI
The platform uses a coordinated multi-agent architecture where specialized agents collaborate to investigate security incidents and generate investigation outcomes.
Pydantic AI
Used for:
- Structured investigation outputs
- Data validation
- Agent communication
- Investigation reporting
- Workflow orchestration
Model Context Protocol (MCP)
Used for:
- Security investigation tools
- Threat intelligence enrichment
- Incident analysis workflows
- Agent-to-tool communication
- Extensible security integrations
Local LLM Ready Architecture
The platform is designed for future integration with local Large Language Models including:
- Ollama
- Qwen
- On-premise Security LLM Deployments
This architecture enables future AI-generated investigation summaries and analyst recommendations while maintaining local control of security data.
Backend Technologies
- Python
- FastAPI
- Pydantic AI
- MCP
- REST APIs
- JSON Investigation Pipeline
- Multi-Agent Workflow Engine
- GitHub Actions CI/CD
Frontend Technologies
- React
- Vite
- React Router
- Axios
- Socket.IO
- Responsive Security Dashboard
API Endpoints
Platform Statistics
GET /statistics
High Priority Cases
GET /high-priority
Case Search
GET /cases
Case Details
GET /case/{case_id}
CI/CD
GitHub Actions automatically validates the platform by:
- Installing project dependencies
- Verifying Python syntax
- Executing the Multi-Agent Orchestrator
- Validating investigation workflow functionality
- Ensuring successful builds before deployment
Learning Objectives
This project demonstrates practical implementation of:
- Agentic AI Architectures
- Security Operations Center (SOC) Workflows
- Multi-Agent Systems
- Pydantic AI
- Model Context Protocol (MCP)
- Threat Intelligence
- MITRE ATT&CK
- FastAPI Development
- React Dashboards
- CI/CD Pipelines
- Security Automation
Future Enhancements
- Ollama + Qwen Investigation Agent
- AI-Generated Executive Summaries
- Automated Threat Hunting
- Advanced Correlation Rules
- Database Persistence
- Docker Deployment
- Cloud-Native Security Integrations
Author
Navid Ghobadpour
Agentic AI Security Operations Platform
Built to explore the intersection of Cybersecurity, Agentic AI, Multi-Agent Systems, Pydantic AI, MCP, Threat Intelligence, and Security Automation.
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