About akramIOT/MCP_AI_SOC_Sher
akramIOT/MCP_AI_SOC_Sher is an MCP server published by akramIOT in the Data category: aI SOC Security Threat analysis using MCP Server.
The repository has 10 stars and 5 forks, with the last commit a year ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues before depending on it in production.
Install
git clone https://github.com/akramIOT/MCP_AI_SOC_SherThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
Ask AI
Ask AI about akramIOT/MCP_AI_SOC_Sher
Powered by Claude · Grounded in docs
Security checks
Static checks derived from the repository 5 hours ago. Heuristics on the source, not an audit.
- ✓README presentThe repository ships documentation.
- ✓License declaredUsable under a stated license.
- ✓Tests presentA test suite exists in the repository.
- !Dependencies pinnedNo lockfile. Installs can pull different code over time.
- ✓No dynamic code executionSource scan found no eval, dynamic Function, or shell execution.
- !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.
Releases
README
MCP AI SOC Sher
A 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.
Features
- Text2SQL Conversion: Convert natural language queries to optimized SQL
- Multiple Interfaces: Support for STDIO, SSE, and REST API
- Security Threat Analysis: Built-in SQL query security analysis
- Multiple Database Support: Connect to SQLite or Snowflake databases
- Streaming Responses: Real-time query processing feedback
- SOC Monitoring: Security Operations Center monitoring capabilities
Installation
pip install mcp-ai-soc-sher
Quick Start
# Set your OpenAI API key
import os
os.environ["OPENAI_API_KEY"] = "your-api-key-here"
# Use as local server
from mcp_ai_soc_sher.local import LocalMCPServer
server = LocalMCPServer()
server.start()
# Or run from command line
# mcp-ai-soc --type local --stdio --sse
Command Line Usage
# Run local server with STDIO interface
mcp-ai-soc --type local --stdio
# Run local server with SSE interface
mcp-ai-soc --type local --sse
# Run remote server with REST API
mcp-ai-soc --type remote
Configuration
Create a .env file with your configuration:
OPENAI_API_KEY=your_openai_api_key_here
MCP_DB_URI=sqlite:///your_database.db
MCP_SECURITY_ENABLE_THREAT_ANALYSIS=true
See the documentation for all configuration options.
Example
import json
import requests
# Query the server
response = requests.post(
"http://localhost:8000/api/sql",
headers={"Content-Type": "application/json", "X-API-Key": "your-api-key"},
json={
"query": "Find all suspicious login attempts in the last 24 hours",
"optimize": True,
"execute": True
}
)
# Process the response
result = response.json()
print(f"SQL Query: {result['sql']}")
if result['results']:
print("Results:")
for row in result['results']:
print(row)
Security Features
- Rule-based and AI-powered SQL query security analysis
- Detection of potential SQL injection attacks
- Sensitive table access monitoring
- Configurable security levels and actions
License
MIT License with Additional Conditions. Copyright (c) 2025 Akram Sheriff.
See LICENSE for details.
Contributing
Contributions are welcome! Please see CONTRIBUTING.md for guidelines.
README fetched from the repository 5 hours ago. The original is authoritative.