Proactive Threat Hunting in Sentinel
Understanding the Concept
Threat hunting in Microsoft Sentinel is a proactive approach to finding threats that evade automated detections. Hunters use KQL queries against ingested data, guided by threat intelligence, industry reports, and behavioral hypotheses.
The hunting page provides built-in queries organized by MITRE ATT&CK tactics. Custom queries can be created and saved. Livestream allows real-time monitoring of query results. Bookmarks capture interesting findings for later investigation.
Hunting notebooks powered by Jupyter provide advanced analysis capabilities including machine learning, statistical analysis, and visualization. They integrate with Python libraries for complex threat analysis.
Key Points
- Proactive search for threats beyond automated detections
- Built-in hunting queries organized by MITRE ATT&CK
- Custom KQL queries for hypothesis-driven hunting
- Livestream for real-time query monitoring
- Bookmarks capture findings for incident creation
- Jupyter notebooks for advanced ML-based analysis
Threat Hunting Process
Intelligence
Review threat feeds and industry reports
Hypothesis
Form testable hypothesis about threats
Hunt
Execute KQL queries against data
Analyze
Review results and bookmark findings
Act
Create incidents or detection rules
Why This Matters in Real Organizations
Automated detections catch known patterns, but sophisticated attackers use novel techniques. Proactive hunting discovers threats that have been living in your environment undetected, often for weeks or months.
Common Mistakes to Avoid
Interview Tips
- Describe your threat hunting methodology
- Share specific examples of threats discovered through hunting
Exam Tips (SC-200)
- Know the hunting workflow in Sentinel
- Understand bookmark and livestream functionality
- Know how to create analytics rules from hunting queries
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