Custom hunting queries in Microsoft Sentinel using Kusto Query Language (KQL) enable security analysts to proactively search for threats across their environment. KQL is a powerful query language designed for exploring large datasets and identifying suspicious patterns or anomalies.
To create cust…Custom hunting queries in Microsoft Sentinel using Kusto Query Language (KQL) enable security analysts to proactively search for threats across their environment. KQL is a powerful query language designed for exploring large datasets and identifying suspicious patterns or anomalies.
To create custom hunting queries, analysts begin by accessing the Hunting section in Microsoft Sentinel. Here, they can write queries that examine data from various sources including security events, sign-in logs, and network traffic.
A basic hunting query structure includes the table name, time range filtering, and specific conditions. For example: SecurityEvent | where TimeGenerated > ago(7d) | where EventID == 4625 | summarize FailedLogins = count() by Account, Computer | where FailedLogins > 10. This query searches for accounts with excessive failed login attempts.
Key KQL operators for hunting include 'where' for filtering data, 'summarize' for aggregating results, 'project' for selecting specific columns, and 'join' for combining data from multiple tables. The 'extend' operator allows analysts to create calculated fields, while 'parse' helps extract specific values from strings.
Effective hunting queries should focus on specific tactics from the MITRE ATT&CK framework. Analysts can search for lateral movement by examining authentication patterns, persistence mechanisms through registry modifications, or data exfiltration through unusual outbound connections.
Best practices include adding metadata to queries such as descriptions, tactics, and techniques for documentation purposes. Analysts should test queries against historical data to validate their effectiveness before deploying them operationally.
Custom queries can be saved as hunting queries or converted to analytics rules for automated detection. When a query identifies potential threats, analysts can promote results to incidents for further investigation. This proactive approach helps organizations detect sophisticated attacks that might evade traditional signature-based detection methods, strengthening overall security posture.
Create Custom Hunting Queries with KQL
Why It Is Important
Custom hunting queries are essential for proactive threat detection in security operations. While built-in detections catch known threats, sophisticated attackers often use novel techniques that evade standard rules. Security analysts who can write custom KQL (Kusto Query Language) queries can:
• Identify previously unknown threats and attack patterns • Investigate suspicious activities across large datasets efficiently • Create tailored detections specific to their organization's environment • Respond faster to emerging threat intelligence
What It Is
KQL (Kusto Query Language) is the query language used in Microsoft Sentinel, Microsoft Defender for Endpoint, and other Microsoft security products. Custom hunting queries are KQL statements written by analysts to search through security data and identify potential threats, anomalies, or indicators of compromise.
These queries can be saved, shared with the team, and even converted into scheduled analytics rules for automated detection.
How It Works
Key KQL Operators for Hunting:
• where - Filters rows based on conditions • project - Selects specific columns to display • summarize - Aggregates data using functions like count(), sum(), avg() • join - Combines data from multiple tables • extend - Creates calculated columns • order by - Sorts results • take/limit - Returns a specified number of rows • render - Visualizes data as charts
Common Hunting Scenarios:
• Searching for failed login attempts: SigninLogs | where ResultType != 0 • Finding processes spawned by Office applications: DeviceProcessEvents | where InitiatingProcessFileName in~ ('winword.exe', 'excel.exe') • Identifying rare processes: DeviceProcessEvents | summarize count() by ProcessCommandLine | where count_ == 1
Key Tables to Know:
• SecurityEvent - Windows security events • SigninLogs - Azure AD sign-in data • DeviceProcessEvents - Process creation events • DeviceNetworkEvents - Network connection data • DeviceFileEvents - File operations • EmailEvents - Email metadata
Exam Tips: Answering Questions on Create Custom Hunting Queries with KQL
1. Know the operator syntax - Understand when to use where vs summarize vs project. Questions often test whether you can select the correct operator for a given scenario.
2. Understand time filtering - Remember that ago() function is used for time-based filtering, such as where TimeGenerated > ago(7d).
3. Master the summarize operator - Many exam questions focus on aggregation. Know how to use count(), dcount(), sum(), and how to group results using by.
4. Recognize table names - Be familiar with which tables contain which data types. Questions may ask where to find specific event types.
5. Understand join types - Know the difference between inner join, leftouter, rightouter, and fullouter joins.
6. Pay attention to case sensitivity - Use =~ for case-insensitive comparisons and == for case-sensitive ones.
7. Look for efficiency hints - When asked about query performance, remember that filtering early with where clauses reduces data processing.
8. Bookmarks and hunting workflow - Understand that interesting results can be bookmarked and added to incidents for further investigation.
9. Practice reading query logic - Exam questions may present a query and ask what results it will return. Read the query step by step, following the pipe flow.
10. Remember the pipe character - KQL uses the pipe | to chain operations, and operations execute from top to bottom, left to right.