Proactive Threat Hunting Framework: Step-by-Step Guide

Threat hunting fills the gap automated alerts leave behind. Attackers can move laterally, disable backups, and stage data theft while standard detections stay quiet, so teams need a repeatable way to look for what controls miss.

The play here is structure. A threat hunting framework turns scattered investigation into a hypothesis-driven, repeatable process, one that finds what existing defenses missed across managed and in-house environments.

Three established frameworks shape most hunting programs today: Sqrrl, PEAK, and TaHiTI. A six-step process at the end shows how to put them into practice.

How hunting differs from automated detection

Threat hunting works alongside automated detection, adding a structured, hypothesis-driven layer for finding adversary activity that existing defenses have missed. Hunting is human-led and hypothesis-driven, distinct from a re-run of security information and event management (SIEM) alerts.

• Automated detection catches known-bad patterns.
• Hunting finds unknown-bad activity: behavior that slips past signatures, rules, and behavioral baselines because no one wrote a detection for it yet.

Here’s the thing: without a framework, hunts become inconsistent one-off efforts that depend entirely on who ran them.

Why a framework matters: Repeatability, maturity, and proof

Frameworks turn one-off hunts into a measurable program. They make hunts repeatable across analysts and environments, measure maturity through models like the Hunting Maturity Model (HMM), and produce documented proof that proactive strategies are happening. At scale, that documentation pays dividends the moment leadership, auditors, or prospects start asking hard questions.

Established threat hunting frameworks

Several frameworks deliver that promise, each solving the same challenge in different ways: how to make threat hunting repeatable, measurable, and easier to improve over time. The right fit depends on operational reality, including team maturity, intelligence capability, and the complexity of the environments being hunted.

The sections below show where each model fits operationally and what each one emphasizes most. Some prioritize a simple hunt-to-detection loop, while others lean harder on intelligence workflows or baseline analysis.

Sqrrl: The original four-stage hunting loop

Sqrrl is a foundational four-stage hunting loop and remains a practical starting point for structured threat hunting.

The core design is an iterative cycle:

1. Hypothesis creation (Stage 1),
2. Investigation via linked datasets (Stage 2),
3. Uncovering new behavioral patterns and tactics, techniques, and procedures (TTPs) (Stage 3),
4. Converting those patterns into automated detection rules (Stage 4).

Stage 4 is the critical handoff: every manual discovery becomes permanent automated coverage, which means each completed loop makes the next hunt more focused.

Sqrrl also introduced the Hunting Maturity Model (HMM), which ranges from HM0, relying entirely on automated alerts, to HM4, where automation stays top of mind when creating new hunting techniques. The HMM helps teams assess each environment individually and sequence investments accordingly.

Bottom line: Sqrrl is the right starting point for teams new to structured hunting who need a simple, proven loop to build from.

PEAK: Prepare, Execute, Act with Knowledge

PEAK builds on Sqrrl’s foundation by defining three distinct hunt types instead of one.

1. Hypothesis-driven hunts work like the classic Sqrrl approach.
2. Baseline hunts define what normal looks like in an environment, then flag deviations. They matter most when teams take on a new environment, post-acquisition, or post-migration.
3. Model-Assisted Threat Hunting (M-ATH) applies machine learning to detect patterns across large datasets that hypothesis-driven analysis alone would miss.

Whichever hunt type fits the situation, PEAK structures the work in three phases: Prepare, Execute, and Act. Knowledge runs continuously across all three, including threat intelligence, organizational expertise, and prior experience that inform every phase.

The Act phase specifies concrete deliverables for every hunt, including recreatable documentation, new or improved detections, documented gaps, and executive summaries.

What this looks like in practice: even a single administrator can start with hypothesis-driven hunts, then scale into baseline and M-ATH hunts as capability grows.

TaHiTI: Targeted Hunting with Threat Intelligence

Where PEAK organizes hunts by type, TaHiTI organizes them around intelligence: threat intelligence is both a primary trigger input and an output of hunting. Three phases, Initiate, Hunt, and Finalize, structure the workflow from trigger processing through real-time intelligence enrichment to documented outputs, and every hunt generates new intelligence that feeds back into future prioritization and detection engineering.

TaHiTI works well for teams with dedicated threat intelligence capability and for organizations in regulated industries where compliance-driven documentation is a requirement.

MITRE ATT&CK as a hunting reference

Sqrrl, PEAK, and TaHiTI all assume a shared language for describing adversary behavior. MITRE ATT&CK provides that language and serves as a widely used technique reference for cyber threat intelligence programs. The practical guidance is clear: do not attempt 100% ATT&CK coverage.

Map existing SIEM detections to ATT&CK techniques using ATT&CK Navigator, identify gaps, then overlay adversary group profiles relevant to the industries you support. Threat group techniques you can’t currently detect become your prioritized hunt backlog. Free tools from the Cybersecurity and Infrastructure Security Agency (CISA), such as Decider, simplify the mapping process using guided questions.

A six-step threat hunting framework

Framework choice matters, but execution is where threat hunting either compounds value or turns into sporadic investigation. Here’s why that matters: a repeatable process keeps hunts grounded in available telemetry, produces usable outputs, and makes the work easier to run across multiple environments.

Threat hunting only works when the telemetry foundation is already in place. Before any hunting begins, confirm three readiness markers:

• Endpoint detection and response (EDR) is deployed and reporting on all managed endpoints, providing the visibility hunters need.
• Windows Event Logs are centrally collected, including authentication events and PowerShell logging, since these surface the activity that matters most.
• A documented baseline of normal activity exists for each environment, allowing deviation-based hunts to produce signal instead of noise.

Once that foundation is in place, the six steps below turn hunting from an ad hoc activity into a repeatable program.

Step 1: Form a hypothesis

A hypothesis is a written, testable statement about suspected adversary behavior, grounded in intelligence or observed anomaly. Three input sources drive hypothesis quality. ATT&CK-mapped threat intelligence identifies which threat groups target the sector and what techniques they use. Environmental anomalies show deviations from baseline that have not yet triggered alerts. Situational context covers elevated-risk periods like infrastructure changes or acquisitions.

Step 2: Identify and collect data sources

Map data availability to the hypothesis before executing. Required sources include EDR telemetry, Windows Event Logs, DNS logs, and firewall/proxy logs. Enhancing sources include Active Directory logs, cloud access logs, and NetFlow data. Document coverage gaps for each one. An environment without EDR and centralized logging cannot be effectively hunted, and that risk belongs in writing.

Step 3: Investigate using tools and techniques

With data sources mapped, four analytical techniques separate hunting from alert triage. Stack counting sorts events by frequency to surface outliers, while frequency analysis detects beaconing patterns invisible to signature-based detection. Clustering reveals patterns hidden at the individual event level. Parent-child process analysis flags deviations like Word spawning PowerShell or LSASS spawning unexpected child processes.

Step 4: Analyze findings and validate threats

Enrich every finding with asset context, cross-reference EDR telemetry for corroboration, and check for the same behavior across other managed systems. Require corroboration from multiple independent data sources before escalating, in line with CISA validation guidance from the federal incident response playbook. Every confirmed false positive produces a documented tuning action, or it will recur.

Step 5: Respond, contain, and remediate

Confirmed incidents need a complete handoff package, including indicators of compromise (IOCs), affected systems with timeline, ATT&CK technique mappings, and scope assessment. Here’s why that matters: a confirmed incident in one place should trigger an immediate IOC sweep across every other managed environment. Shared management infrastructure can become a path for lateral movement when one compromised account or system spans multiple environments. Blast radius assessment is not optional.

Step 6: Document and automate for future hunts

Beyond response, every hunt contains reusable knowledge. Document the hypothesis, data sources queried, techniques applied, findings, and disposition. The upshot: the hunt-to-detection conversion pipeline is where hunting compounds its value. Confirm a TTP, convert to a SIEM detection rule, validate against historical data, then link the rule back to the originating hunt for future tuning context.

Operationalizing the framework with N-able

These six steps require tooling that spans the full attack lifecycle, covering each phase without forcing teams to stitch together disconnected products.

Before the attack, N-able N-central shrinks the attack surface that hunting has to cover. Automated patching closes known vulnerabilities at scale, integrated EDR generates the endpoint telemetry hunters depend on, and built-in vulnerability management identifies the high-exposure systems hunters can prioritize.

During the attack, Adlumin Security Operations handles detection, investigation, and automated response. The 24/7 SOC pairs analyst eyes-on coverage with AI-driven detection, and automated containment kicks in on confirmed incidents while hunters pursue hypothesis-driven leads. Adlumin combines SIEM, security orchestration, automation, and response (SOAR), and behavioral analytics in one platform, so correlation across data sources happens natively rather than through manual export and stitching.

After the attack, Cove Data Protection closes the response loop with immutable, cloud-isolated backups that ransomware cannot reach or alter. TrueDelta technology produces backups up to 60x smaller than image-based alternatives, making 15-minute intervals practical without saturating bandwidth. Standby images shorten the gap between confirmed compromise and restored operations.

Turn hunting from practice into program

Even with the right framework and tooling in place, a threat hunting program only delivers value when it runs consistently and produces measurable output. Pick a framework that matches your current maturity: Sqrrl for foundational structure, PEAK for modern flexibility, and TaHiTI for intelligence-heavy programs. Build the telemetry foundation, and commit to documenting every hunt so the investment compounds over time. The gap between teams that hunt proactively and those that wait for alerts is widening. Contact us to see how N-able supports threat hunting across the full attack lifecycle.

Frequently Asked Questions About Threat Hunting Frameworks

A strong hunting program raises the same operational questions every time: staffing, cadence, automation limits, telemetry retention, and proof that the work is paying off. The answers below keep the focus on what makes hunting sustainable across real environments.

What is the minimum team size needed to run a threat hunting program?

A periodic model where team members dedicate a few hours per week to hunting is a viable starting point. Hypothesis-driven hunts are the simplest way for smaller teams to begin.

How often should threat hunts be conducted?

Scheduling depends on environment size and risk profile, but structuring hunts as scheduled engagements documented separately from reactive monitoring keeps the program consistent.

Can AI replace human threat hunters?

AI accelerates data ingestion, anomaly flagging, and summarization. Hypothesis generation and analytical judgment stay human, particularly the recognition of fundamental errors and hallucinated connections.

What data retention is needed to support effective hunting?

Most teams discover retention gaps only when they need historical data during an active hunt. Set minimum retention per environment before launching the program, not after a gap surfaces at the worst possible time.

How do I measure whether a threat hunting program is working?

A working program produces novel detections, documents data and detection gaps, and improves continuous monitoring coverage over time. Measuring hunting by alert volume processed misses the point entirely.