jguimera SecurityCopilotMCPServer

jguimera SecurityCopilotMCPServer avatar

by jguimera

MCP Server that integrates with Security Copilot, Sentinel and other tools (in the future). It enhance the process of developing , testing and uploading Security Copilot artifacts.

What is jguimera SecurityCopilotMCPServer

Security Copilot and Sentinel MCP Server

A Python-based MCP server using FastMCP library that provides integration with Microsoft Security Copilot and Microsoft Sentinel using Azure Identity Authentication. Diagram

Overview

This project implements an MCP server that enables:

  • Running KQL queries against Microsoft Sentinel
  • Uploading/Updating Microsoft Security Copilot skillsets/plugins
  • Running prompts and skills in Microsoft Security Copilot

The server acts as a bridge between development environments and Microsoft Security Copilot, allowing for testing, deployment, and execution of skills and plugins. It uses SSE as transport layer for the MCP server. There are many use cases for the current integration. One of the most interesting ones is to support the development, test and deployment of Security Copilot KQL Skills.

AgentFlow1 AgentFlow2

Features

  • Sentinel Integration: Execute KQL queries against your Sentinel workspace
  • Security Copilot Management:
    • List existing skillsets/plugins
    • Upload new or update existing skillsets/plugins
    • Run prompts or skills within Security Copilot
  • Authentication Support: Multiple authentication methods including interactive browser, client secret, and managed identity

Roadmap

The next features will include:

  • Promptbook test and Update
  • Run Advance Hunting queries in Defender XDR

Prerequisites

  • Python 3.8+
  • Microsoft Sentinel workspace
  • Microsoft Security Copilot access
  • Appropriate Azure permissions for Sentinel and Security Copilot

Installation

  1. Clone the repository:

    git clone https://github.com/jguimera/SecurityCopilotMCPServer.git
    cd SecurityCopilotMCPServer
    
  2. Install dependencies:

    pip install -r requirements.txt
    
  3. Create a .env file with the following configuration:

    #Add App Reg to use ClientID and Secret authentication
    #AZURE_TENANT_ID=your_tenant_id
    #AZURE_CLIENT_ID=your_client_id
    #AZURE_CLIENT_SECRET=your_client_secret
    SENTINEL_SUBSCRIPTION_ID=your_subscription_id
    SENTINEL_RESOURCE_GROUP=your_resource_group
    SENTINEL_WORKSPACE_NAME=your_workspace_name
    SENTINEL_WORKSPACE_ID=your_workspace_id
    #Authentication Options: interactive, client_secret
    AUTHENTICATION_TYPE=interactive
    

Usage

Starting the Server

Run the MCP server:

python server.py

To run tests before starting the server:

python server.py --run-tests

Available Tools

The MCP server provides the following tools:

  1. run_sentinel_query: Execute KQL queries in Sentinel
  2. get_skillsets: List skillsets in Security Copilot
  3. upload_plugin: Upload or update a skillset/plugin
  4. run_prompt: Run a prompt or skill in Security Copilot

MCP Client Config for Cursor

You can use this MCP server from the Client of your choice. In this repo you can find intructions and config files for Cursor.

Add the .cursor folder inside your client project to enable the MCP tools. This folder contains two files:

  1. Cursor Project Rules (securitycopilotdev.mdc): This file include some Custom Cursor Rules to help the agents in the process definition and understanding user prompts.
  2. MCP Client Configuration (mcp.json): File that connects Cursor to the MCP server.

You can invoke the tool directly using /tool_name parameter1="Value of the tool parameter" For example: /run_prompt content="List the most recent risky users"

More info: https://docs.cursor.com/context/model-context-protocol#configuring-mcp-servers

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add some amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

License

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

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Frequently Asked Questions

What is MCP?

MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications, providing a standardized way to connect AI models to different data sources and tools.

What are MCP Servers?

MCP Servers are lightweight programs that expose specific capabilities through the standardized Model Context Protocol. They act as bridges between LLMs like Claude and various data sources or services, allowing secure access to files, databases, APIs, and other resources.

How do MCP Servers work?

MCP Servers follow a client-server architecture where a host application (like Claude Desktop) connects to multiple servers. Each server provides specific functionality through standardized endpoints and protocols, enabling Claude to access data and perform actions through the standardized protocol.

Are MCP Servers secure?

Yes, MCP Servers are designed with security in mind. They run locally with explicit configuration and permissions, require user approval for actions, and include built-in security features to prevent unauthorized access and ensure data privacy.

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