Advanced Hunting with KQL
Understanding the Concept
Advanced hunting in Defender XDR allows analysts to proactively search for threats using Kusto Query Language (KQL). It provides access to 30 days of raw telemetry data across endpoints, email, identity, and cloud apps in a unified schema.
KQL is a read-only query language for exploring large datasets. Key operators include where (filtering), project (column selection), summarize (aggregation), join (combining tables), and render (visualization).
The schema includes tables like DeviceEvents, DeviceProcessEvents, EmailEvents, IdentityLogonEvents, and CloudAppEvents.
Key Points
- Advanced hunting provides 30 days of raw telemetry
- KQL operators: where, project, summarize, join, extend, render
- Key tables: DeviceEvents, DeviceProcessEvents, EmailEvents, IdentityLogonEvents
- Queries can be saved, shared, and converted to custom detection rules
- KQL is heavily tested on the SC-200 exam
Advanced Hunting Workflow
Hypothesis
Formulate a threat hypothesis
Query Design
Write KQL against schema tables
Execution
Run query and analyze results
Detection Rule
Convert to custom detection
Why This Matters in Real Organizations
Automated detections catch known threats, but APTs use novel techniques. Proactive hunting with KQL discovers hidden threats and creates new detection rules before damage occurs.
Common Mistakes to Avoid
Interview Tips
- Be ready to write basic KQL queries
- Discuss hypothesis-driven threat hunting methodology
- Share threat discovery examples from hunting
Exam Tips (SC-200)
- Practice writing KQL queries extensively
- Know key advanced hunting tables
- Understand custom detection rule creation from queries
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