Comprehensive Guide to Integrating Custom Connectors in Microsoft Sentinel
Microsoft Sentinel is one of the best tools for keeping your organization safe. Its ability to connect with many services is powerful. But what if you need to connect to a niche or custom log source? That’s where custom connectors come in. They let you bring nearly any data into Sentinel, even if it’s not supported out of the box. This guide explains how to use various methods to build custom connectors, when to pick each, and best practices to make your setup smooth and reliable.
Understanding Microsoft Sentinel and the Need for Custom Connectors
What is Microsoft Sentinel?
Sentinel is a cloud-native security info and event management (SIEM) platform. It gathers logs from many sources—including Azure AD, AWS, and Office 365—and transforms that data into helpful alerts and dashboards. Most common services have built-in connectors, making setup quick for popular tools.
Why Use Custom Connectors?
What if your business relies on a unique app or on-premise device that generates logs Sentinel doesn’t support? Or if your security team needs to analyze data from niche systems? Custom connectors fill this gap. They help centralize all your log sources, making sure you meet compliance standards and spot threats faster.
Real-World Scenario
Imagine a client using a legacy syslog server. It logs authentication attempts, but Sentinel doesn’t have a built-in way to import this data. Using custom connectors, you can build a pipeline—perhaps with Azure functions—that pushes this info into Sentinel in real time. It’s a simple solution with big benefits.
Comparing Custom Connector Methods in Microsoft Sentinel
Overview of Integration Options
There are several ways to connect custom data sources to Sentinel:
- Codeless connector framework (CCF)
- Azure Monitor Agent
- Logstash-based pipelines
- REST API with log injection
- Azure Functions for custom logic
Factors to Consider When Choosing
- Team’s skills: Do you have programmers or prefer low-code tools?
- Data volume: Will you send a few logs or huge amounts?
- Infrastructure complexity: Are your sources on-prem or cloud?
- Cost and overhead: Will this be a quick setup or ongoing effort?
Quick Summary Table
|
Method
|
Best For
|
Pros
|
Cons
|
Skill Level
|
|
Codeless Framework
|
Low-tech, JSON logs
|
Easy, no coding required
|
Less flexible
|
Non-developers
|
|
Azure Monitor Agent
|
Structured logs on servers
|
Simple, integrates easily
|
Limited to predictable formats
|
Basic administration
|
|
Logstash pipelines
|
Complex, multi-source environments
|
Highly customizable
|
Requires setup, maintenance
|
Intermediate Devs
|
|
Log Injection API
|
Real-time, custom data formats
|
Very flexible
|
Requires programming
|
Advanced developers
|
|
Azure Functions
|
Custom, scalable connectors
|
Full control
|
Need coding skills
|
Skilled developers
|
Codeless Connector Framework (CCF)
What is CCF?
Codeless Connector Framework lets you bring data into Sentinel just by writing simple JSON files. It’s designed for teams that want to avoid coding. You define how data should map to Sentinel schemas through configuration files, and Sentinel does the rest.
When to Use CCF
Think of CCF as a drag-and-drop for logs. If your source outputs well-structured JSON, like many SaaS platforms do, you can set up a connector quickly. It’s perfect for organisations with limited coding skills or when logging sources don't change often.
How It Works
You create a JSON configuration that guides Sentinel on parsing incoming logs. Sentinel then continuously monitors the health of these connectors, alerting you if there's an issue. No fuss, no extra infrastructure.
Benefits & Limitations
Pros:
- No infrastructure needed
- Easy for non-technical teams
- Good for steady, JSON-formatted data
Cons:
- Less flexible for complex or unpredictable data
- Not suitable for real-time high-volume feeds
Agent-Based Log Collection with Azure Monitor Agent
What is Azure Monitor Agent?
This agent installed on your servers gathers logs stored as text files—like system or application logs—and pushes them to Sentinel. It saves time for teams already using Azure monitoring tools.
How to Set It Up
- Install Azure Monitor Agent on target servers
- Define Data Collection Rules specifying what logs to send
- Routing logs into Sentinel via these rules
Practical Examples
Suppose your firewall saves logs as JSON files in a folder. Connecting the agent to this folder enables Sentinel to analyze or alert on these logs. Or, logs from web apps and Windows servers follow predictable patterns, making them perfect for this method.
Strengths & Weaknesses
- Strengths: Simple setup, familiar for Azure users, no new tools needed
- Limitations: Log formats must be predictable, not ideal for massive or complex pipelines
Logstash and Data Flow Pipelines
Using Logstash
Logstash, from Elastic, is a powerful tool to process logs before they reach Sentinel. It can connect to different systems like Kafka or cloud storages. You can filter, transform, and route logs as needed.
How It Works
Set up Logstash to pull logs from sources, process them, then forward to Sentinel via API or connectors. It’s like a pipeline that filters water—only here, the water is logs.
Ideal Use Cases & Examples
You might have logs from Google Cloud Storage or multiple applications with different formats. Logstash can unify this data, clean it up, and send it to Sentinel efficiently.
Benefits and Tradeoffs
It offers deep control and scalability but requires managing extra infrastructure and expertise. It works best when your data flows are complex and need detailed transformations.
API-Based Data Injection with Log Injection API
What is the Log Injection API?
This API lets you push data directly into Sentinel using code. You craft detailed API requests and send logs in real time, making it highly flexible.
Implementation Tips
You’ll need to develop scripts or applications that authenticate with Sentinel, format logs properly, and handle errors. It’s ideal for custom tools or vendors wanting tight integration.
Real-World Applications
Security vendors or specialized tools can send logs directly into Sentinel, tailored to their needs—like a custom endpoint for a proprietary system.
Best Practices
- Properly format data before sending
- Monitor API success rates
- Secure your API keys
Building Custom Connectors with Azure Functions
What Are Azure Functions?
Azure Functions let you run code in the cloud without managing servers. Use them to create custom log collections, parse different formats, or combine data from multiple sources.
How to Implement
- Write functions that call external APIs (like VMware Carbon Black)
- Process data (XML, CSV, JSON)
- Push data into Sentinel
Use Cases & Examples
Suppose you have a security solution that outputs logs in XML. An Azure Function can parse this XML and send relevant info to Sentinel. It scales easily as your data grows.
Advantages & Challenges
This setup offers maximum flexibility. But it does need some coding skills. Plus, costs depend on how many times your functions run.
Data Normalization and Parsing with ASIM
What is ASIM?
The Advanced Security Information Model (ASIM) standardizes how logs are structured. Using ASIM helps Sentinel understand various custom logs, making searching and alerting simpler.
How to Use ASIM
You can parse logs early with Azure Functions or log filters, then map data into the ASIM schema. Once normalized, Sentinel can process your data like native logs.
Why It Matters
Using ASIM improves query speed and eases security investigations. Think of it as translating all logs into the same language, no matter their source.
When to Use Each Custom Connector Method
- For quick, low-cost deployments with minimal coding, pick CCF or Logic Apps.
- When collecting predictable server logs, the Azure Monitor Agent is your best friend.
- For customized, high-volume data with complex transformation needs, Logstash or API calls work better.
- When dealing with proprietary or unique data sources, Azure Functions are the top option.
Best Practices for Implementing Custom Connectors in Microsoft Sentinel
- Plan before building: Know your sources and how they fit into Sentinel schemas.
- Test thoroughly: Use Sentinel’s monitoring tools to catch problems early.
- Monitor health: Keep an eye on connector status and logs.
- Automate deployment: Use DevOps or ARM templates for consistency.
- Document your setup: Make sure others can understand and maintain your connectors.
Summary:
Custom connectors open up endless possibilities for your security team. Whether you prefer low-code options like CCF and Logic Apps or need advanced control with Azure Functions and APIs, there’s a way to fit your needs. The key is understanding your data sources, skill level, and operational constraints. With the right approach, you can centralize all your logs into Sentinel, making your security stronger and more effective. Take the time to plan, test, and monitor your integrations—they’ll pay off in better security insights and faster threat detection.
This guide gives you a complete view of how to build, choose, and manage custom connectors in Microsoft Sentinel. By blending best practices with practical examples, you’ll be ready to extend Sentinel’s capabilities and keep your organization safe.
