Beyond Gut Feeling: Threat Hunting Becomes a SOC Discipline
17.09.2026 threat-huntingsocpeak-frameworkmaturity-model
Threat hunting used to be treated as a luxury exercise for teams with spare capacity: an experienced analyst, a hunch, a few hours in the SIEM. In 2026, that picture no longer holds. The 2026 CrowdStrike Global Threat Report highlights a 42% increase in zero-day vulnerabilities exploited before public disclosure, while Statista projects the global cost of cybercrime will reach $13.82 trillion by 2028. Organizations that keep hunting without structure are burning exactly the analyst hours their SOCs can least afford to waste.
PEAK: Structure Over Lucky Guesses
In response to this gap, Splunk’s PEAK framework has emerged as the de facto standard alongside the older Sqrrl and TaHiTI models. PEAK, an acronym for “Prepare, Execute, and Act with Knowledge,” incorporates three distinct types of hunts, and each PEAK hunt follows a three-stage process: Prepare, Execute, and Act. What matters is not just the sequence but the role knowledge plays throughout: each phase integrates Knowledge, which could be in the form of organizational or business expertise, threat intelligence, prior experience of the hunter(s), or findings from the current hunt. Unlike rigid checklists, PEAK is explicitly designed as a toolkit: hunters can skip, reorder, or add steps to each phase, tailoring their approach to suit the situation at hand.
For SOC leadership, this means PEAK doesn’t impose a fixed ritual — it provides a shared vocabulary for documenting, comparing, and feeding hunts back into detection engineering. That’s the difference between a one-off exercise and a self-improving program.
Maturity Is Not a Vanity Metric
Alongside methodology, every team needs a yardstick for where it stands. David Bianco’s Hunting Maturity Model remains the reference point here. The SANS Institute’s threat hunting maturity model describes five levels from HMM0 (initial, primarily reactive) to HMM4 (leading, with automated hunt workflows integrated into SOC operations). Most organizations are well below that bar: most organizations start at HMM1; the goal is not to reach HMM4 immediately but to progress steadily by making hunting a consistent, documented practice rather than an occasional exercise. Crucially for the IR interface, hunting and incident response are not separate silos. Hunts that discover threats transition into incident response workflows, and incident response investigations that reveal gaps in detection coverage generate hypotheses for future hunts.
The Real Bottleneck Is Capacity
The uncomfortable truth for 2026 is that methodology alone doesn’t solve the problem. Execution capacity now limits programs more than methodology design; Sqrrl, MITRE, TaHiTI, and PEAK all codify the hypothesis-test-verdict-feedback loop, yet hypothesis lists sit unexecuted because hunting competes with alert triage for the same experienced analysts. This is precisely where agent-executed hunting is gaining traction: AI-executed hunting rests on a specific division of labor — humans define hypotheses, agents execute investigations. Early field reports back up the promise: hunts that consumed up to 40 hours of manual analyst work compress to roughly one hour.
For IR teams, the practical takeaway is threefold: adopt PEAK or a comparable framework, honestly assess your current HMM level, and then invest deliberately in execution capacity — whether through automation or through disciplined prioritization of which hypotheses actually get worked. Polishing the methodology while ignoring the capacity question just means staying stuck at HMM1, no matter how elegant the framework diagram looks on the wall.
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