Creating custom log tables in Microsoft Sentinel workspace allows security analysts to ingest and analyze data from sources that are not covered by built-in connectors. This capability is essential for organizations with unique data sources or specialized security tools.
To create custom log table…Creating custom log tables in Microsoft Sentinel workspace allows security analysts to ingest and analyze data from sources that are not covered by built-in connectors. This capability is essential for organizations with unique data sources or specialized security tools.
To create custom log tables, you need to use the Data Collection Rules (DCR) and Data Collection Endpoints (DCE) framework, which is part of the Azure Monitor Logs infrastructure that Sentinel leverages.
The process begins by accessing your Log Analytics workspace associated with Sentinel. Navigate to the Tables section under Settings, where you can create a new custom table. Custom tables follow a naming convention ending with '_CL' to distinguish them from standard tables.
When defining a custom table, you must specify the schema, including column names and data types. Common data types include string, int, long, real, datetime, and boolean. Proper schema design ensures efficient querying and storage optimization.
After creating the table structure, you configure data ingestion through the Logs Ingestion API or Azure Monitor Agent. The Logs Ingestion API allows applications to send data via HTTP POST requests to your DCE. You must create a DCR that maps incoming data fields to your custom table columns and applies any necessary transformations using KQL.
Authentication for data ingestion requires Azure Active Directory app registration with appropriate permissions. The application needs the Monitoring Metrics Publisher role on the DCR.
Once data flows into your custom table, you can query it using Kusto Query Language in the Sentinel Logs blade. You can also incorporate this data into analytics rules, workbooks, and hunting queries to enhance your security monitoring capabilities.
Best practices include planning retention policies, considering data volume costs, and implementing proper parsing at ingestion time to optimize query performance. Regular validation ensures data quality and completeness for effective security operations.
Create Custom Log Tables in Sentinel Workspace
Why It Is Important
Creating custom log tables in Microsoft Sentinel is essential for security operations because it allows organizations to ingest and analyze data from non-standard sources that are not covered by built-in connectors. This capability enables security analysts to have complete visibility across their entire environment, including proprietary applications, legacy systems, and custom data sources. Custom tables help maintain compliance requirements and support comprehensive threat detection across all organizational assets.
What It Is
Custom log tables are user-defined tables in the Log Analytics workspace that store data ingested from custom sources. These tables follow a specific naming convention with a _CL suffix (Custom Log) and can be created using the Data Collection Rules (DCR) or the Log Analytics Data Collector API. Custom tables allow you to define your own schema and structure to accommodate unique data formats from various sources.
How It Works
The process involves several key steps:
1. Define the Table Schema: You must specify the columns, data types, and structure of your custom table. This can be done through the Azure portal, ARM templates, or PowerShell.
2. Create Data Collection Rules (DCR): DCRs define how data should be collected, transformed, and routed to your custom table. They support transformations using KQL to filter or modify data before ingestion.
3. Configure Data Ingestion: Use the Logs Ingestion API or Azure Monitor Agent with DCR to send data to your custom table. The API endpoint and authentication (using Azure AD) must be properly configured.
4. Table Retention and Access: Configure retention policies and set appropriate permissions for who can query the custom table data.
Key Components: - Tables API: Used to create and manage custom table schemas - Logs Ingestion API: Sends data to custom tables via DCR - Data Collection Endpoints (DCE): Regional endpoints for data ingestion - Transformations: KQL queries that process data during ingestion
Exam Tips: Answering Questions on Create Custom Log Tables in Sentinel Workspace
Key Facts to Remember: - Custom tables always end with _CL suffix - The Logs Ingestion API requires Azure AD authentication using a service principal or managed identity - Data Collection Rules are required for the new ingestion method - Transformations in DCR use source as the input table name in KQL - You need both a Data Collection Endpoint and Data Collection Rule for API-based ingestion
Common Exam Scenarios: - When asked about ingesting data from a custom application, look for answers involving DCR and Logs Ingestion API - Questions about table naming conventions should reference the _CL suffix requirement - For cost optimization questions, remember that transformations can filter data before storage - If asked about schema changes, note that you can add columns but changing existing column types requires creating a new table
Watch Out For: - Legacy Data Collector API (HTTP Data Collector API) is being deprecated; focus on the newer Logs Ingestion API approach - Custom tables require proper RBAC permissions including Monitoring Metrics Publisher role for data ingestion - Transformation queries must output the same column names expected by the destination table
Remember: When comparing solutions, the modern approach using DCR provides more flexibility with transformations, better performance, and enhanced security compared to older methods.