SOC POC with LangGraph

🔒 Intelligent Security Operations Center - Multi-Agent Workflow System

An advanced Security Operations Center (SOC) proof-of-concept built on LangGraph, featuring autonomous AI agents for alert processing, threat analysis, and incident response. This system demonstrates how modern AI can enhance security operations through intelligent automation, human-AI collaboration, and continuous learning.

🏗️ Architecture Overview

The SOC POC implements a sophisticated multi-layered architecture designed for scalability, reliability, and intelligent decision-making:

Core Framework

• State Management: Centralized state handling with versioning and audit trails
• Workflow Engine: LangGraph-powered orchestration for complex multi-agent workflows
• Configuration: Dynamic policy-driven configuration management

Agent Ecosystem

Six specialized AI agents working in harmony:

1. Ingestion Agent - Multi-source alert collection with deduplication
2. Triage Agent - Intelligent alert prioritization and initial classification
3. Analysis Agent - Deep threat investigation using ReAct reasoning loops
4. Human-in-the-Loop - Structured analyst collaboration and escalation
5. Response Agent - Automated containment and remediation actions
6. Learning Agent - Continuous model improvement and knowledge extraction

Tool Orchestration Layer

Unified interface to security tools:

• SIEM Integration - Splunk, QRadar, Sentinel connectivity
• Threat Intelligence - IOC enrichment and reputation services
• Sandbox Analysis - Automated malware detonation
• EDR/XDR Tools - Endpoint investigation and response

🚀 Key Features

Intelligent Decision Making

• Confidence-Based Routing - Alerts flow through agents based on confidence thresholds
• Policy-Driven Workflows - Configurable decision points with business logic
• Adaptive Thresholds - Dynamic adjustment based on historical performance

Advanced State Management

EnhancedStateObject:
├── alert_id&raw_alert
├── enriched_data&triage_status ├── confidence_score&FP/TP_indicators
├── workflow_history&agent_executions
├── human_feedback&response_actions
└── metadata&audit_trail

Asynchronous Processing

• Concurrent Tool Execution - Parallel security tool queries
• Retry Mechanisms - Robust error handling and fallback strategies
• Caching Layer - Redis-powered performance optimization
• Rate Limiting - API protection and resource management

Human-AI Collaboration

• Structured Feedback Interface - Clear escalation and review processes
• Role-Based Access Control - Analyst, senior analyst, and manager workflows
• SLA Tracking - Response time monitoring and alerting
• Knowledge Transfer - Human insights fed back to learning systems

🔄 Workflow Process

graph TD
%% Enhanced State Definition
State[<b>Enhanced State Object</b><br/>• alert_id<br/>• raw_alert<br/>• enriched_data<br/>• triage_status<br/>• confidence_score<br/>• FP/TP_indicators<br/>• workflow_history<br/>• agent_executions<br/>• human_feedback<br/>• response_actions<br/>• metadata]
%% Enhanced Nodes with Async Support
Ingestion[<b>Ingestion Agent</b><br/>• Async Sources<br/>• Batching<br/>• Deduplication<br/>• Rate Limiting]
Triage[<b>Triage Agent</b><br/>• Rule Engine<br/>• ML Scoring<br/>• Thresholds<br/>• Fallback]
Correlation[<b>Correlation Agent</b><br/>• Async Queries<br/>• Caching<br/>• Timeouts<br/>• Retry Logic]
Analysis[<b>Analysis Agent</b><br/>• ReAct Loop<br/>• Tool Orchestration<br/>• Fallback to Human<br/>• Reasoning Logs]
HumanLoop[<b>Human-in-the-Loop</b><br/>• Structured Feedback<br/>• Role-Based Access<br/>• Escalation Levels<br/>• SLA Tracking]
Response[<b>Response Agent</b><br/>• Playbook Engine<br/>• Approval Workflow<br/>• Rollback Support<br/>• Action Audit]
Learning[<b>Learning Agent</b><br/>• Model Versioning<br/>• Training Pipeline<br/>• Performance Metrics<br/>• A/B Testing]
Close[<b>Close Alert</b><br/>• State Validation<br/>• Audit Trail<br/>• Archive Process<br/>• Metrics Collection]
%% Enhanced Decision Points with Thresholds
IsFP{Confidence > 80%<br/>AND FP Indicators?<br/>Policy: FP_THRESHOLD}
NeedsCorrelation{Confidence 40-70%<br/>OR Needs Context?<br/>Policy: CORRELATION_POLICY}
NeedsAnalysis{Confidence < 60%<br/>OR Complex Alert?<br/>Policy: ANALYSIS_POLICY}
NeedsHuman{Confidence Grey Zone<br/>OR High Risk?<br/>Policy: HUMAN_REVIEW_POLICY}
NeedsResponse{Confirmed Threat<br/>AND Auto-Response<br/>Enabled?<br/>Policy: RESPONSE_POLICY}
%% Enhanced Tool Orchestration
Tools[<b>Tool Orchestration Layer</b><br/>• Async Execution<br/>• Retry Strategies<br/>• Caching Layer<br/>• Metrics Collection<br/>• Timeout Handling<br/>• Fallback Logic]
%% Storage Layer
Storage[<b>Storage Layer</b><br/>• PostgreSQL<br/>• Redis Cache<br/>• Vector DB<br/>• State History<br/>• Audit Logs]
%% Monitoring & Observability
Monitoring[<b>Monitoring & Observability</b><br/>• Metrics<br/>• Tracing<br/>• Logging<br/>• Alerts<br/>• Dashboards]
%% Enhanced Flow with Async and Feedback Loops
Ingestion -->|Initialize State| State
State -->|New Alert| Triage
Triage -->|Update State| State
State -->|Triage Complete| IsFP
IsFP -->|Yes| Close
IsFP -->|No| NeedsCorrelation
NeedsCorrelation -->|Yes| Correlation
NeedsCorrelation -->|No| NeedsAnalysis
Correlation -->|Update State| State
State -->|Correlation Complete| NeedsAnalysis
NeedsAnalysis -->|Yes| Analysis
NeedsAnalysis -->|No| NeedsHuman
Analysis -->|ReAct Loop| State
State -->|Analysis Complete| NeedsHuman
NeedsHuman -->|Yes| HumanLoop
NeedsHuman -->|No| NeedsResponse
HumanLoop -->|Update State| State
State -->|Human Feedback| NeedsResponse
NeedsResponse -->|Yes| Response
NeedsResponse -->|No| Learning
Response -->|Update State| State
State -->|Response Complete| Learning
Learning -->|Update State| State
State -->|Learning Complete| Close
%% Enhanced Tool Connections with Orchestration
Triage -.->|Call via Orchestrator| Tools
Correlation -.->|Async Call via Orchestrator| Tools
Analysis -.->|ReAct via Orchestrator| Tools
HumanLoop -.->|Call via Orchestrator| Tools
Response -.->|Call via Orchestrator| Tools
Learning -.->|Call via Orchestrator| Tools
%% Storage Connections
State <-->|Persist/Load| Storage
Tools <-->|Cache/Store| Storage
%% Monitoring Connections
State -.->|State Changes| Monitoring
Tools -.->|Tool Metrics| Monitoring
Triage -.->|Agent Metrics| Monitoring
Correlation -.->|Agent Metrics| Monitoring
Analysis -.->|Agent Metrics| Monitoring
HumanLoop -.->|Agent Metrics| Monitoring
Response -.->|Agent Metrics| Monitoring
Learning -.->|Agent Metrics| Monitoring
%% Feedback Loops
HumanLoop -.->|Feedback| Learning
Learning -.->|Improved Models| Analysis
Learning -.->|Improved Models| Triage
Learning -.->|Improved Models| Correlation
%% Styling
classDef agent fill:#e1f5fe,stroke:#01579b,stroke-width:2px
classDef decision fill:#fff3e0,stroke:#e65100,stroke-width:2px
classDef state fill:#f3e5f5,stroke:#4a148c,stroke-width:2px
classDef tools fill:#e8f5e9,stroke:#1b5e20,stroke-width:2px
classDef storage fill:#fff8e1,stroke:#ff8f00,stroke-width:2px
classDef monitoring fill:#fce4ec,stroke:#880e4f,stroke-width:2px
classDef terminal fill:#ffebee,stroke:#b71c1c,stroke-width:2px
class Ingestion,Triage,Correlation,Analysis,HumanLoop,Response,Learning agent
class IsFP,NeedsCorrelation,NeedsAnalysis,NeedsHuman,NeedsResponse decision
class State state
class Tools tools
class Storage storage
class Monitoring monitoring
class Close terminal

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Architecture Layers

graph TB
subgraph "Core Framework"
Core[Core Framework]
State[State Management]
Workflow[Workflow Engine]
Config[Configuration]
end
subgraph "Agent Layer"
IngestionMod[Ingestion Module]
TriageMod[Triage Module]
AnalysisMod[Analysis Module]
HumanMod[Human Loop Module]
ResponseMod[Response Module]
LearningMod[Learning Module]
end
subgraph "Tool Layer"
ToolOrchestrator[Tool Orchestrator]
SIEMTools[SIEM Tools]
IntelTools[Intel Tools]
SandboxTools[Sandbox Tools]
EDRTools[EDR Tools]
end
subgraph "Storage Layer"
StateStorage[State Storage]
CacheLayer[Cache Layer]
VectorDB[Vector DB]
AuditLogs[Audit Logs]
end
subgraph "Monitoring Layer"
Metrics[Metrics Collection]
Tracing[Distributed Tracing]
Logging[Structured Logging]
Alerting[Alerting System]
end
Core --> State
Core --> Workflow
Core --> Config
Workflow --> IngestionMod
Workflow --> TriageMod
Workflow --> AnalysisMod
Workflow --> HumanMod
Workflow --> ResponseMod
Workflow --> LearningMod
IngestionMod --> ToolOrchestrator
TriageMod --> ToolOrchestrator
AnalysisMod --> ToolOrchestrator
HumanMod --> ToolOrchestrator
ResponseMod --> ToolOrchestrator
LearningMod --> ToolOrchestrator
ToolOrchestrator --> SIEMTools
ToolOrchestrator --> IntelTools
ToolOrchestrator --> SandboxTools
ToolOrchestrator --> EDRTools
State --> StateStorage
ToolOrchestrator --> CacheLayer
LearningMod --> VectorDB
State --> AuditLogs
IngestionMod --> Metrics
TriageMod --> Metrics
AnalysisMod --> Metrics
HumanMod --> Metrics
ResponseMod --> Metrics
LearningMod --> Metrics
Metrics --> Tracing
Metrics --> Logging
Metrics --> Alerting

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1. Alert Ingestion

graph LR
A[Alert Sources] --> B[Ingestion Agent]
B --> C[State Initialization]
C --> D[Deduplication]
D --> E[Rate Limiting]
E --> F[Triage Queue]

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2. Intelligent Triage

graph TD
A[Alert Input] --> B{Rule Engine}
B --> C[ML Scoring]
C --> D{Confidence Score}
D -->|>80%| E[Auto-Close FP]
D -->|40-80%| F[Correlation Queue]
D -->|<40%| G[Analysis Queue]
E --> H[Learning Feedback]
F --> I[Correlation Agent]
G --> J[Analysis Agent]

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3. Contextual Correlation

graph LR
A[Alert] --> B[Correlation Agent]
B --> C[Async Queries]
C --> D[Threat Intel APIs]
C --> E[SIEM Historical Data]
C --> F[Asset Information]
D --> G[Enrichment Results]
E --> G
F --> G
G --> H[Temporal Analysis]
H --> I[Entity Resolution]
I --> J[Updated State]

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4. Deep Analysis (ReAct Loop)

graph TD
A[Analysis Agent] --> B[Reasoning]
B --> C{Need More Data?}
C -->|Yes| D[Action: Query Tools]
D --> E[Tool Execution]
E --> F[Observation]
F --> B
C -->|No| G[Conclusion]
G --> H{Confidence Level}
H -->|High| I[Auto Response]
H -->|Medium| J[Human Review]
H -->|Low| K[Escalate to Senior]
I --> L[Response Agent]
J --> M[Human-in-Loop]
K --> M

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5. Human Escalation

graph TD
A[Human Review Required] --> B{Risk Level}
B -->|Critical| C[Immediate Escalation]
B -->|High| D[Senior Analyst Queue]
B -->|Medium| E[Standard Review Queue]
C --> F[Manager/CISO Alert]
D --> G[Senior Analyst]
E --> H[Analyst]
F --> I[Structured Feedback]
G --> I
H --> I
I --> J{Decision}
J -->|Approve| K[Response Agent]
J -->|Deny| L[Close Alert]
J -->|Need More Info| M[Back to Analysis]

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6. Automated Response

graph TD
A[Response Triggered] --> B[Playbook Selection]
B --> C{Approval Required?}
C -->|Yes| D[Approval Workflow]
C -->|No| E[Execute Actions]
D --> F{Approved?}
F -->|Yes| E
F -->|No| G[Log Decision & Close]
E --> H[Block IP/Domain]
E --> I[Quarantine File]
E --> J[Disable Account]
E --> K[Network Isolation]
H --> L[Action Audit]
I --> L
J --> L
K --> L
L --> M{Success?}
M -->|Yes| N[Update State]
M -->|No| O[Rollback & Alert]
N --> P[Learning Agent]
O --> Q[Human Intervention]

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7. Continuous Learning

graph TD
A[Learning Agent] --> B[Collect Feedback]
B --> C[Human Feedback]
B --> D[Agent Performance]
B --> E[Response Outcomes]
C --> F[Model Training Data]
D --> F
E --> F
F --> G[Model Versioning]
G --> H{A/B Testing}
H -->|Champion| I[Deploy New Model]
H -->|Challenger| J[Performance Analysis]
J --> K{Better Performance?}
K -->|Yes| I
K -->|No| L[Keep Current Model]
I --> M[Update Agent Configs]
L --> N[Log Results]
M --> O[Performance Monitoring]
N --> O
O --> P[Metrics Dashboard]

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📊 Monitoring & Observability

Comprehensive Telemetry

• Metrics Collection: Agent performance, tool latency, accuracy rates
• Distributed Tracing: End-to-end workflow visibility
• Structured Logging: Searchable audit trails and debugging
• Real-time Dashboards: Operations center visibility

Key Performance Indicators

• Mean Time to Detection (MTTD)
• Mean Time to Response (MTTR)
• False Positive Rate
• Agent Accuracy Scores
• Human Escalation Rate
• Tool Utilization Metrics

🛠️ Technology Stack

Core Technologies

• LangGraph: Multi-agent workflow orchestration
• LangChain: LLM integration and tool connectivity
• PostgreSQL: Primary state and audit storage
• Redis: Caching and session management
• Vector Database: Similarity search and embeddings

AI/ML Components

• Large Language Models: GPT-4, Claude for reasoning
• Custom ML Models: Specialized threat detection
• Embedding Models: Semantic similarity analysis
• Classification Models: Alert categorization

Security Tool Integrations

• SIEM Platforms: Splunk, IBM QRadar, Microsoft Sentinel
• Threat Intelligence: VirusTotal, MISP, commercial feeds
• Sandbox Solutions: Cuckoo, Joe Sandbox, Falcon Sandbox
• EDR/XDR: CrowdStrike, SentinelOne, Microsoft Defender

🚦 Getting Started

Prerequisites

Python 3.11+
PostgreSQL 14+
Redis 7+
Docker & Docker Compose
Security tool API credentials

Quick Setup

# Clone repository
git clone https://github.com/your-org/soc-langgraph-poc
cd soc-langgraph-poc
# Install dependencies
pip install -r requirements.txt
# Configure environment
cp .env.example .env
# Edit .env with your API keys and database URLs# Initialize database
python scripts/init_db.py
# Start services
docker-compose up -d
# Launch SOC workflow
python main.py

Configuration

Key configuration areas:

• Agent Policies: Confidence thresholds and routing logic
• Tool Credentials: API keys and connection strings
• Workflow Rules: Business logic and escalation procedures
• Learning Settings: Model update frequencies and training data

📈 Use Cases & Benefits

Automated Threat Detection

• 24/7 Operations: Continuous alert processing without human fatigue
• Consistent Analysis: Standardized investigation procedures
• Rapid Response: Sub-minute detection-to-containment cycles

Analyst Augmentation

• Decision Support: AI-powered recommendations with explanations
• Workload Optimization: Focus on high-value investigative work
• Knowledge Scaling: Junior analysts with senior-level insights

Operational Excellence

• Reduced False Positives: Intelligent filtering and correlation
• Improved MTTR: Faster incident response through automation
• Audit Compliance: Complete workflow documentation and traceability

🔮 Roadmap & Future Enhancements

Planned Features

• Multi-Tenant Architecture: Support for multiple customer environments
• Advanced ML Models: Custom threat detection model training
• Integration Marketplace: Plug-and-play security tool connectors
• Mobile Interface: Analyst mobile app for on-the-go response

Research Areas

• Federated Learning: Cross-organization threat intelligence sharing
• Explainable AI: Enhanced transparency in AI decision-making
• Attack Graph Analysis: Multi-stage attack detection and visualization

🤝 Contributing

We welcome contributions from the security and AI communities:

1. Issue Reporting: Bug reports and feature requests
2. Code Contributions: Pull requests with improvements
3. Tool Integrations: New security tool connectors
4. Documentation: Enhanced guides and examples

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• LangGraph Team: For the excellent multi-agent framework
• Security Community: For threat intelligence and best practices
• Open Source Contributors: For the foundational tools and libraries

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