KQL Use Cases
Last updated
Last updated
Kusto Query Language (KQL) is a powerful and versatile query language designed to analyse large volumes of log and telemetry data in realtime. For security analysts, KQL is an essential tool because it enables precise and efficient querying of data in platforms like Microsoft Sentinel, Defender, and Azure Monitor. Its intuitive syntax allows analysts to filter, aggregate, and correlate data quickly, making it ideal for identifying patterns, anomalies, and potential threats. With its ability to handle complex queries, visualise results, and integrate with security workflows, KQL empowers analysts to detect and investigate incidents faster and more effectively.
Moreover, KQL supports advanced data analysis techniques, such as time-series analysis, joins, and machine learning-based anomaly detection, which are critical for proactive threat hunting and incident response. It is highly scalable, making it capable of querying massive datasets generated by modern IT infrastructures. Learning KQL equips security analysts with the skills to customise dashboards, create detailed reports, and automate threat detection, saving time and reducing alert fatigue. Its user-friendly design and depth of functionality make KQL an invaluable tool for security professionals looking to enhance their analytical capabilities and strengthen their organisation’s security posture.
Throughout the following sections, various examples and use cases will demonstrate the flexibility and capabilities of the KQL query language and how it can be integrated into standard operating procedures.
The following are the subsections of the KQL Use Cases and investigative workflows based on the Mitre Attack Framework:
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