MCP AI SOC Sher

1# MCP AI SOC Sher
2 
3A powerful AI-driven Security Operations Center (SOC) Text2SQL framework  based  MCP Server (Local and Remote) for converting natural language Prompts  to SQL queries dynamically, with integrated security threat analysis and monitoring.
4 
5## Features
6 
7- **Text2SQL Conversion**: Convert natural language queries to optimized SQL
8- **Multiple Interfaces**: Support for STDIO, SSE, and REST API
9- **Security Threat Analysis**: Built-in SQL query security analysis
10- **Multiple Database Support**: Connect to SQLite or Snowflake databases
11- **Streaming Responses**: Real-time query processing feedback
12- **SOC Monitoring**: Security Operations Center monitoring capabilities
13 
14## Installation
15 
16```bash
17pip install mcp-ai-soc-sher
18```
19 
20## Quick Start
21 
22```python
23# Set your OpenAI API key
24import os
25os.environ["OPENAI_API_KEY"] = "your-api-key-here"
26 
27# Use as local server
28from mcp_ai_soc_sher.local import LocalMCPServer
29 
30server = LocalMCPServer()
31server.start()
32 
33# Or run from command line
34# mcp-ai-soc --type local --stdio --sse
35```
36 
37## Command Line Usage
38 
39```bash
40# Run local server with STDIO interface
41mcp-ai-soc --type local --stdio
42 
43# Run local server with SSE interface
44mcp-ai-soc --type local --sse
45 
46# Run remote server with REST API
47mcp-ai-soc --type remote
48```
49 
50## Configuration
51 
52Create a `.env` file with your configuration:
53 
54```
55OPENAI_API_KEY=your_openai_api_key_here
56MCP_DB_URI=sqlite:///your_database.db
57MCP_SECURITY_ENABLE_THREAT_ANALYSIS=true
58```
59 
60See the [documentation](docs/configuration.md) for all configuration options.
61 
62## Example
63 
64```python
65import json
66import requests
67 
68# Query the server
69response = requests.post(
70    "http://localhost:8000/api/sql",
71    headers={"Content-Type": "application/json", "X-API-Key": "your-api-key"},
72    json={
73        "query": "Find all suspicious login attempts in the last 24 hours",
74        "optimize": True,
75        "execute": True
76    }
77)
78 
79# Process the response
80result = response.json()
81print(f"SQL Query: {result['sql']}")
82if result['results']:
83    print("Results:")
84    for row in result['results']:
85        print(row)
86```
87 
88## Security Features
89 
90- Rule-based and AI-powered SQL query security analysis
91- Detection of potential SQL injection attacks
92- Sensitive table access monitoring
93- Configurable security levels and actions
94 
95## License
96 
97MIT License with Additional Conditions. Copyright (c) 2025 Akram Sheriff.
98 
99See [LICENSE](LICENSE) for details.
100 
101## Contributing
102 
103Contributions are welcome! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
104