Identify threats using Kusto Query Language (KQL)

Kusto Query Language (KQL) is a powerful query language used in Microsoft Sentinel and Microsoft Defender for identifying and investigating security threats. As a Security Operations Analyst, mastering KQL is essential for effective threat detection and response. KQL enables analysts to query larg… Kusto Query Language (KQL) is a powerful query language used in Microsoft Sentinel and Microsoft Defender for identifying and investigating security threats. As a Security Operations Analyst, mastering KQL is essential for effective threat detection and response. KQL enables analysts to query large datasets stored in Log Analytics workspaces, where security logs from various sources are collected. These sources include Azure Active Directory, Microsoft 365, endpoint devices, firewalls, and custom applications. To identify threats using KQL, analysts typically start with basic queries that filter security events. For example, querying the SecurityEvent table to find failed login attempts helps detect brute force attacks. The syntax involves specifying the table name, applying filters using the 'where' operator, and selecting relevant columns with 'project'. Key KQL operators for threat hunting include: - 'where': Filters data based on conditions - 'summarize': Aggregates data to identify patterns - 'join': Combines data from multiple tables - 'extend': Creates calculated columns - 'render': Visualizes query results Analysts can identify suspicious activities by looking for anomalies such as unusual login times, multiple failed authentication attempts, or connections from unexpected geographic locations. Time-based analysis using 'ago()' and 'between()' functions helps establish baselines and detect deviations. Advanced threat identification involves correlating events across multiple data sources. For instance, combining sign-in logs with endpoint data can reveal compromised accounts being used for lateral movement. Microsoft Sentinel provides built-in analytics rules that use KQL queries to generate alerts when specific threat patterns are detected. Security analysts can also create custom detection rules tailored to their organization's environment. Effective KQL queries for threat identification should be optimized for performance, especially when querying large datasets. Using time filters early in queries and limiting result sets improves query execution speed, enabling faster threat response.