kql-expert

To optimize slow KQL queries in Azure Monitor, you must identify performance bottlenecks like inefficient joins or broad time filters. This Skill analyzes complex queries and provides an optimized version to prevent timeouts and ensure efficient data analysis.

Validating KQL against Microsoft Sentinel table schemas is done using Python scripts to check field names and data types. This ensures your analytics rules parse correctly against the destination tables without runtime errors.

The best way to convert Splunk SPL queries to KQL is through automated migration assistance. This translates Splunk search commands into equivalent Kusto Query Language operators for use within Microsoft Sentinel and M365 Defender.

Using ASIM parsers for source-agnostic threat hunting involves applying Advanced SIEM Information Models to normalize disparate logs. This allows you to write analytics rules that query standardized fields across different data sources.

You can use this for developing Microsoft Sentinel detection rules by validating your KQL logic against table schemas. It assists in creating compliant and effective analytics rules tailored for threat hunting and security monitoring.

It works with M365 Defender advanced hunting queries by validating your KQL syntax against M365 Defender table schemas. This ensures accurate threat hunting and data analysis across the Microsoft security ecosystem.