Microsoft Sentinel hunting queries are powerful tools that enable security analysts to proactively search for threats across their environment before alerts are triggered. These queries use Kusto Query Language (KQL) to explore data and identify suspicious activities or potential security incidents…Microsoft Sentinel hunting queries are powerful tools that enable security analysts to proactively search for threats across their environment before alerts are triggered. These queries use Kusto Query Language (KQL) to explore data and identify suspicious activities or potential security incidents.
To create a hunting query in Sentinel, navigate to the Threat Management section and select Hunting. Here you can access built-in queries or create custom ones. When building a custom query, you define the KQL statement that searches through your connected data sources like Azure Active Directory logs, firewall logs, or endpoint data. Each query should include relevant metadata such as name, description, tactics mapped to MITRE ATT&CK framework, and required data sources.
The query structure typically includes filters for specific time ranges, entity types like user accounts or IP addresses, and conditions that indicate anomalous behavior. For example, you might create a query to detect unusual login patterns or data exfiltration attempts.
Monitoring hunting queries involves several key practices. First, run queries regularly to identify new threats. Sentinel allows you to bookmark interesting results for further investigation. These bookmarks can be promoted to incidents when confirmed as genuine threats. Second, track query performance using the results count and last run time displayed in the hunting dashboard.
Livestream functionality enables real-time monitoring of query results, alerting analysts when matches occur. You can also convert successful hunting queries into scheduled analytics rules for automated detection.
Best practices include organizing queries by threat category, documenting findings, and refining queries based on false positive rates. Collaboration features allow teams to share effective queries across the organization. Regular review of hunting query effectiveness ensures your threat detection capabilities evolve alongside emerging attack techniques and changing infrastructure.
Create and Monitor Hunting Queries in Microsoft Sentinel
Why is This Important?
Threat hunting is a proactive security practice that allows analysts to search for threats that evade existing detection rules. In Microsoft Sentinel, hunting queries enable security teams to identify suspicious activities, discover new attack patterns, and uncover hidden threats before they cause damage. This skill is essential for SC-200 exam candidates as it demonstrates advanced threat detection capabilities.
What Are Hunting Queries?
Hunting queries are KQL (Kusto Query Language) queries designed to proactively search through your security data for potential threats. Unlike analytics rules that run automatically, hunting queries are typically executed manually by analysts investigating hypotheses about potential security incidents. Sentinel provides built-in hunting queries aligned with the MITRE ATT&CK framework, and you can create custom queries tailored to your environment.
How Hunting Queries Work
Key Components: - Query: Written in KQL to search across log data - Entity Mapping: Links query results to entities like accounts, hosts, or IP addresses - Tactics and Techniques: Maps to MITRE ATT&CK framework categories - Bookmarks: Allow you to save interesting results for further investigation - Livestream: Enables real-time monitoring of query results
Creating a Hunting Query: 1. Navigate to Microsoft Sentinel > Threat Management > Hunting 2. Click New Query 3. Enter the KQL query in the query editor 4. Configure entity mappings to identify relevant security entities 5. Assign MITRE ATT&CK tactics and techniques 6. Save and run the query
Monitoring Hunting Queries: - Use Livestream to monitor queries in real-time for up to 30 minutes - Review the Results column to see how many records match - Add results to Bookmarks for preservation and investigation - Promote bookmarks to full Incidents when threats are confirmed
Exam Tips: Answering Questions on Create and Monitor Hunting Queries in Sentinel
1. Know the difference between hunting queries and analytics rules: Hunting is proactive and manual, while analytics rules are automated detections.
2. Understand Bookmarks: Remember that bookmarks preserve hunting results and can be promoted to incidents. They maintain evidence even if underlying data is deleted.
3. Livestream functionality: Know that Livestream allows real-time monitoring of hunting queries with a maximum duration of 30 minutes per session.
4. MITRE ATT&CK mapping: Questions may ask about categorizing queries by tactics like Initial Access, Persistence, or Lateral Movement.
5. Entity mapping importance: Understand that entity mapping connects query results to specific entities, enabling correlation and investigation.
6. Built-in vs Custom queries: Sentinel includes pre-built hunting queries that can be cloned and modified for custom needs.
7. KQL basics: Be familiar with common KQL operators used in hunting such as where, project, summarize, and join.
8. Notebook integration: Know that hunting queries can be used alongside Jupyter Notebooks for advanced analysis.
9. When to use hunting: Hunting is appropriate for hypothesis-driven investigations and exploring data that existing rules may miss.
10. Results interpretation: Pay attention to questions about what actions to take after finding suspicious results, such as creating bookmarks or promoting to incidents.