Formulating An Intelligence-Driven Threat Hunting Methodology
summary
Proposes an operational, intelligence-driven hunting loop that ties adversary operations, visibility gaps, and organizational risk into testable hypotheses, then turns successful hunts into automated detections. Gives concrete examples: business email compromise detected by email header/DKIM analysis; ransomware chains using IcedID, Cobalt Strike and PsExec with a measured ~3.5-hour time-to-ransom from initial compromise to payload. Recommends instrumenting host, network, and artifact telemetry, hunting where gaps allow, and translating validated hunt queries into detection engineering.
AI-written summary · about
chapters (14)
- 0:00Introduction to threat hunting
- 1:30Defining threat hunting
- 3:25Prerequisites for hunting
- 5:16Visibility and hypothesis testing
- 6:44Formulating testable hypotheses
- 9:13Understanding adversary operations
- 11:22Data sources and telemetry
- 13:57Bridging visibility gaps
- 15:50Risk-based prioritization
- 17:22Hunting example: BEC
- 19:15Hunting example: Ransomware
- 23:40From hunting to detection
- 25:28Conclusion and takeaways
- 26:06Q&A session
related talks
31mThreat Hunting — Demystified
36mSo You Want to Build a Threat Hunt Program
42mPractical Threat Hunting: Straight Facts and Substantial Impacts
40mHunt and Gather: Developing Effective Threat Hunting Techniques
48mPractical Learnings for Threat Hunting and Improving Your Security Posture
38mAdversary Detection Pipelines: Finally Making Your Threat Intel Useful
31mDon't Boil the Ocean: Using MITRE ATT&CK to Guide Hunting Activity
29mThrough the Eyes of the Adversary: How to Build a Threat Intel Program
