Step-by-Step Guide to Microsoft Sentinel Architecture and Data Processing

Security Information and Event Management (SIEM) systems have become increasingly indispensable for organizations of all sizes. Among the cloud-native SIEM solutions, Microsoft Sentinel is a scalable platform that enables security teams to collect, analyze, and correlate security data.

Built on the Azure cloud, Microsoft Sentinel integrates with various data sources and uses advanced analytics and machine learning to provide real-time threat detection and visibility across the entire enterprise.

In this post, we’ll walk through the architectural design and integration process involved in setting up Microsoft Sentinel, specifically while handling custom log ingestion and cybersecurity tool integrations using Azure Function Apps.

Before diving into the architecture, it’s worth asking why so many organizations are turning to Microsoft Sentinel. Let’s take a quick look at why that is.

Why Integrate with Microsoft Sentinel?

Organizations need visibility across diverse environments, cloud, hybrid, or on-premises. Integrating Microsoft Sentinel with external tools and systems helps achieve:

Centralized Visibility: Consolidates security data from multiple sources into one pane of glass.
Automated Threat Detection: Uses built-in rule-based analytics, machine learning models (Fusion), and User and Entity Behavior Analytics (UEBA) to detect suspicious activity.
Custom Alerting and Responses: Enables creation of custom rules, playbooks, and automation to handle security incidents effectively.
Scalability and Flexibility: Being cloud-native, it can scale based on log volume and integrate easily with hundreds of Microsoft and non-Microsoft sources.
Cost-Efficiency: Offers pay-as-you-go and commitment-tier pricing models, which can provide better ROI than traditional SIEMs, especially when paired with other cost-optimization strategies.

Microsoft Sentinel Integration Architecture

Microsoft Sentinel supports multiple integration strategies. However, when it comes to ingesting custom or third-party log data (like a DRP tool, endpoint detection system, or any legacy firewall), Azure Function Apps become an essential component.

Below are two key approaches organizations can use:

Approach 1: Direct Function Apps

This approach is ideal for ingesting and transforming unstructured logs using serverless compute via Azure Function Apps. It's quick to implement and effective for straightforward log formats.

Step-by-Step Workflow:

Create a Function App in the Azure Portal: Set up a Function App to act as the log processing engine, triggered by HTTP requests, storage events, or timers.
Configure Authentication: Secure the Function App using Azure AD, API tokens, or Managed Identities to restrict access to authorized log sources.
Transform Unstructured Logs: Inside the Function App, parse raw logs and convert them into structured data using C#, Python, or PowerShell.
Validate Data Transmission: Send the processed logs to the Log Analytics Workspace and verify field mapping, timestamp formatting, and ingestion status within Microsoft Sentinel.

Approach 2: Custom Schema with Function App

This method is used when incoming logs have a complex structure or require customization to match Sentinel’s schema and analytics capabilities.

Step-by-Step Workflow:

Analyze Existing Log Structure: Review source logs to identify key fields such as timestamps, event types, IP addresses, and severity levels.
Design a Compatible Schema: Create a custom table in Log Analytics with fields aligned to Microsoft Sentinel's detection, alerting, and dashboard needs.
Implement Mapping Logic: Use the Function App to transform source log fields to align with the custom schema defined in your Data Collection Rule (DCR). This includes renaming fields, formatting data, and applying necessary logic before pushing it through the Log Ingestion API via a configured Data Collection Endpoint (DCE).
Validate Log Transformations: Ingest the transformed logs and use KQL queries to ensure data integrity, accurate mappings, and readiness for analytics in Sentinel.

Prerequisites for Successful Integration

Like any integration effort, connecting tools to Microsoft Sentinel has certain prerequisites. We've consolidated them for you to ensure a smooth and successful integration:

Network Accessibility: Ensure source systems or integration components like Function Apps can reach Azure endpoints over the internet or through Private Link, VPNs, or service endpoints as appropriate.
Authentication and Access Control: Use Managed Identities for secure, tokenless authentication when calling the Log Ingestion API. For external sources, use OAuth 2.0 flows to securely retrieve data.
Data Volume Planning: Estimate log ingestion volume for cost and performance optimization.
Custom Table Creation: Use Data Collection Rules (DCRs) to define custom tables if native schemas do not meet your needs. Ensure proper schema design for KQL-based detections.
Function App Scaling: Based on expected load, configure the Function App with proper scaling rules and error handling logic.
Testing and Validation: Use test logs to validate schema mapping, timestamp accuracy, and alert trigger functionality.

Note: If you are having issues with your data ingestion, please refer to our troubleshooting post for more information about resolving common data ingestion issues.

Conclusion

Microsoft Sentinel provides a powerful, flexible, and cloud-native SIEM solution for enterprises aiming to improve their security posture. At Metron, we’ve successfully integrated Microsoft Sentinel across a wide range of environments, from small-scale deployments to complex enterprise infrastructures.

We've worked extensively with Microsoft’s documentation, and more importantly, we've identified what works in real-world scenarios so you don't have to spend hours researching or second-guessing architectural decisions.

If your organization is looking to implement Microsoft Sentinel or is facing challenges integrating it with other platforms, feel free to contact us at connect@metronlabs.com for assistance.