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Introduction:
Security Operations Centers (SOCs) face a persistent challenge: transforming raw telemetry and alerts into actionable intelligence quickly enough to keep pace with adversaries. The recent Microsoft Security Summit 2026 showcased a powerful solution to this bottleneck, demonstrating how Microsoft Sentinel, combined with the Model Context Protocol (MCP), can automate the creation of detailed investigation reports. This approach not only accelerates incident response but also standardizes reporting, allowing analysts to focus on complex threat hunting rather than manual documentation.
Learning Objectives:
- Understand the integration between Microsoft Sentinel and the Model Context Protocol (MCP) for automated reporting.
- Learn how to use VS Code with Copilot and MCP to streamline SOC investigation workflows.
- Access and utilize real-world investigation report templates from GitHub to enhance your security operations.
You Should Know:
1. Leveraging the MCP Sentinel GitHub Repository
The foundation of this automated reporting process is the collection of investigation reports shared by David Alonso Dominguez. These resources provide a practical starting point for any security team looking to implement similar automation.
Step‑by‑step guide explaining what this does and how to use it.
– Clone the Repository: Begin by accessing the provided GitHub link: `https://github.com/davidalonsodominguez/Sentinel-MCP-InvestigationReports` (or the actual repository from the post). Use `git clone https://github.com/davidalonsodominguez/Sentinel-MCP-InvestigationReports.git` to download the templates and scripts.
– Review the Structure: Navigate into the repository and examine the folder structure. Look for directories containing JSON or Markdown templates for incident reports, as well as any Python or PowerShell scripts designed to interface with the Microsoft Sentinel API.
– Understand the Logic: The core concept involves the MCP acting as a bridge. It receives an incident ID from Sentinel, queries the relevant log data (from Log Analytics Workspaces), and then populates a pre-defined report template. This transforms raw data into a structured narrative.
- Setting Up the MCP Environment with VS Code
To replicate the demo, you need to configure your development environment to communicate with Sentinel using MCP, often facilitated by tools like VS Code with the Cline (or similar) extension.
Step‑by‑step guide explaining what this does and how to use it.
– Install VS Code and Extensions: Ensure you have Visual Studio Code installed. Install the “Cline” extension (or any MCP client extension) which allows VS Code to act as an MCP host.
– Configure MCP Server for Sentinel: You will need an MCP server that can interact with Azure. This often involves setting up a server using Node.js or Python. A typical configuration in the `cline_mcp_settings.json` file might look like this:
{
"mcpServers": {
"azure-sentinel": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-azure-sentinel"],
"env": {
"AZURE_TENANT_ID": "your-tenant-id",
"AZURE_CLIENT_ID": "your-client-id",
"AZURE_CLIENT_SECRET": "your-client-secret",
"AZURE_SUBSCRIPTION_ID": "your-subscription-id",
"AZURE_RESOURCE_GROUP": "your-resource-group",
"AZURE_WORKSPACE_NAME": "your-sentinel-workspace-name"
}
}
}
}
– Authenticate: This configuration uses Service Principal authentication. Ensure your service principal has the “Microsoft Sentinel Reader” and “Log Analytics Reader” roles assigned at the resource group level to query incidents and logs.
3. Automating Incident Enrichment with MCP Queries
Once the connection is established, you can automate the extraction of specific data points from an incident. Instead of manually querying KQL, MCP can run predefined queries and return the results.
Step‑by‑step guide explaining what this does and how to use it.
– Identify the Incident: In your VS Code chat with the MCP client, you can instruct the AI to fetch an incident by its ID. For example: “Fetch incident ID 12345 from Sentinel.”
– Query for Context: The MCP server will execute a query against the Log Analytics Workspace. Under the hood, it might run a KQL query like:
// Example KQL to get alerts related to an incident let incidentId = "12345"; SecurityAlert | where IncidentId == incidentId | project TimeGenerated, AlertName, ProviderName, Entities
– Return Structured Data: The MCP returns this data as structured JSON, which the AI can then interpret and use to populate a report section about “Initial Access” or “Malicious Entities.”
4. Generating the Investigation Report
The final step is using the enriched data to create a human-readable report. This is where the AI (like Copilot or the MCP client’s LLM) synthesizes the raw data into prose.
Step‑by‑step guide explaining what this does and how to use it.
– Create a Report Template: Use the templates from the GitHub repository. A simple Markdown template might look like this:
Incident Report: INC-[bash] Date: [bash] Severity: [bash] Summary [AI-generated summary based on MCP data] Attack Timeline [Timeline of events] Impacted Assets [List of entities] Recommended Actions [Mitigation steps]
– Use AI to Populate: With the MCP server running and the template in context, instruct the AI: “Using the data from incident 12345, generate a full investigation report following this template.” The AI will call the MCP tools to fetch data and then fill in the template.
– Review and Finalize: The generated report can be saved as a `.md` file or exported to PDF. This process reduces a 30-minute manual reporting task to under a minute.
5. Cross-Platform Automation Commands
To make this workflow robust, you may need to run scripts on different operating systems. Below are commands to manage the MCP server environment on both Linux and Windows.
Step‑by‑step guide explaining what this does and how to use it.
– Linux/macOS (Bash): For setting up a Python-based MCP server.
Create a virtual environment python3 -m venv mcp-sentinel-env source mcp-sentinel-env/bin/activate Install required packages (e.g., Azure Identity, MCP SDK) pip install azure-identity azure-monitor-query mcp Run the server (assuming a custom script) python sentinel_mcp_server.py
– Windows (PowerShell): For setting up and running the server.
Create a virtual environment python -m venv mcp-sentinel-env .\mcp-sentinel-env\Scripts\Activate.ps1 Install packages pip install azure-identity azure-monitor-query mcp Run the server python sentinel_mcp_server.py
– Using Azure CLI for Authentication: On any platform, you can also use `az login` for interactive authentication if you are not using a service principal in a headless environment. The MCP server must be configured to pick up these credentials.
What Undercode Say:
- Automation is the New Standard: The demo highlights that the future of SOC operations lies in automating the “last mile” of incident response—the reporting and documentation phase. This frees up highly skilled analysts to perform complex correlation and proactive hunting.
- MCP is a Critical Enabler: The Model Context Protocol acts as a universal adapter, allowing large language models to interact with specific tools like Azure Sentinel securely. This pattern will likely become the standard for integrating AI into enterprise security workflows.
- Practical Resources Matter: The availability of real-world GitHub repositories from experts like David Alonso Dominguez is invaluable. These resources lower the barrier to entry, allowing teams to adopt best practices without starting from scratch.
Prediction:
The integration of AI-driven automation via protocols like MCP will fundamentally reshape the SOC analyst role. In the next two years, we will see a shift from analysts who manually triage and report to “automation engineers” who build and maintain these AI-powered investigative pipelines. The bottleneck will no longer be data analysis, but the accuracy and context-awareness of the automated processes, making the validation of AI-generated reports a critical new skill for security professionals.
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IT/Security Reporter URL:
Reported By: David Alonso – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
