Structured Threat Hunting Methodology

Moving from reactive detection to proactive threat hunting — a systematic approach to finding adversaries before they cause damage.

Threat Hunting: How to Think Like an Attacker Before Damage Happens

Instead of waiting for alarms (being reactive), threat hunters proactively search for suspicious activity. Which is the key difference between responding to an incident versus hunting for threats.

The key is to think like an attacker:

“If I were trying to compromise this network, where would I start?”

Threat hunting doesn’t always happen in isolation. Sometimes it’s conducted as part of an incident response investigation — for example, when suspicious activity has already been reported and the SOC team needs to confirm or scope the threat.

Other times, it might be a proactive hunt kicked off by a manager who notices unusual network behavior and wants to ensure nothing malicious is hiding beneath the surface.

In both cases, the goal is the same: get ahead of the attacker by proactively searching for their tactics, techniques, and procedures (TTPs) before they escalate into full-scale damage.

When I started venturing into threat hunting and incident response, it felt intimidating with all the different information that is available. Going through all the information through SIEM’s and EDR’s (telemetry sources) seemed daunting. So I created for myself a structured process so even complex hunts become manageable and repeatable. It’s a 4 step process that I’m going to walk you through.

Step 1: Develop a Hypothesis

This is where everything begins. A hypothesis gives us an idea of what might have occurred and becomes our primary guide for the hunt.

Without it, the search would be too broad, scattered, and inefficient.

In other words, the hypothesis is the first step in tightening the search.

Example:

“An attacker has gained access to our network and is using Mimikatz to steal credentials.”

Once we have a hypothesis, we ask:

What artifacts, indicators, or entities would exist if this attack happened?
What evidence, if found, would confirm it?

You don’t need to memorize every possibility — this is where Threat Intelligence (TI) helps fill in the details of what techniques and indicators usually look like.

Why Root Cause Matters: The Strategic Purpose of Threat Hunting

It is always important for me to remember that the objective of any incident or threat hunt is to figure out the root cause. This is because this is where the attack started, which means that’s where the vulnerability lies. So for example, if an employee clicked a malicious link which gave an attacker access to the network, or maybe there was an unpatched system, or credential theft — whatever it is. Knowing the entry point of the entity or attacker is the goal because we want to close that entry point (the vulnerability) to prevent attacks like this from happening again. It’s not just about finding the threat — it’s about understanding how they got in so you can actually stop it from happening twice.

Step 2: Identify Evidence Sources

This step flows directly from the hypothesis. Now we take the clues from Step 1 and research what they mean:

What type of attack is this?
What behaviors or indicators normally follow this technique?
Where would traces of this activity be recorded?

From this, we derive the log sources.

Example (Credential Dumping with Mimikatz):

Behavior	Log Source
Suspicious process or file creation	EDR / Sysmon logs
Strange login activity	Windows Security Event Logs
Outbound connections to unknown IPs	Firewall or Proxy logs
Registry changes for persistence	Registry logs

We don’t stop at choosing the log type — we refine further into log fields.

For example:

In authentication logs → focus on Account Name, Logon Type, Timestamp.
In process logs → focus on Parent Process, Command Line, Hash.

The goal is simple: look for evidence that supports the hypothesis and decide whether the activity is truly suspicious or benign.

Tactical Breakdown: How to Execute This Step

When identifying evidence sources, here’s the practical workflow:

Gather threat intelligence about the suspected attack technique
- What are the known indicators? (file names, processes, network signatures)
- What do security researchers say about this TTP?
Map behaviors to log tables

In a SIEM/EDR, there are log tables such as Device-File-Events, Process-Events, Network-Events, Logon-Events, etc.

- Which logs would capture process execution? (Process-Events)
- Which logs would show network activity? (Network-Events, Firewall, Proxy, NetFlow)
- Which logs would reveal persistence? (Registry-Events, Scheduled Tasks)

When we select a table, there would be fields in each table. So our next step would be to get an understanding and research what fields would be pertinent, or go through the log tables within the SIEM/EDR and check what fields are under each table. From there we can view which fields may be relevant. This is important because we have to build our query. All of these steps lead to building a query and knowing what to extract from the log tables to one, confirm our hypothesis and two, create a timeline of the events. Which will help us with our objective of tracing the attack back to its root cause.

Identify specific log fields to investigate
- Not just “check Sysmon” — but “check ProcessName, CommandLine, ParentProcess” in the log table
- Not just “check firewall” — but “check DestinationIP, Port, Timestamp”
Build targeted search queries
- Create specific queries for each log source
- Define what “suspicious” looks like (e.g., ProcessName = powershell.exe AND ParentProcess = excel.exe)

This tactical approach ensures you’re not just collecting logs — you’re extracting the right signals from them.

Step 3: Transform the Data

At this point, we have:

The hypothesis (what attacker behavior we suspect).
The evidence sources (where traces of this behavior would appear).

Now comes the actual hunt — analyzing and correlating the data across sources. This is where if we are using a SIEM or EDR (depending on which telemetry source we are using) the queries that we build now come into play. The query’s would look something like this:

DeviceLogonEvents  // this is the log table. recall examples from our previous step of different log tables such as DeviceNetworkEvents, DeviceFileEvents, DeviceProcessEvents etc.
| where LogonType == 'Network'  // this is filtering to only include events where the LogonType field has the value "Network"
| summarize FailedAttempts = count() by DeviceName, AccountName, ActionType, Protocol  // this groups the data by DeviceName, AccountName, ActionType, and Protocol, and counts the number of times each combination appears, calling it FailedAttempts
| where FailedAttempts > 10  // this filters the summarized results to only show those where the FailedAttempts count is greater than 10
| sort by FailedAttempts desc  // this sorts the remaining results in descending order based on the FailedAttempts count (highest first)

Examples:

If a process spawns PowerShell, was the initiator normal (like a trusted admin tool) or suspicious (like Excel spawning PowerShell)?
If a suspicious command is executed, did it also trigger an outbound network connection?
If a user logged in, was it at an unusual time or from an unusual IP?

Key actions in this step:

Filter noise: Eliminate what’s normal.
Correlate events: Connect activity across logs (process → command → network).
Spot anomalies: Focus on what doesn’t fit the baseline of normal behavior.

A suspicious PowerShell command alone isn’t enough.

But if you connect it to the initiating process, the user who ran it, and an outbound connection — you begin to build a story.

This transformation turns raw logs into meaningful conclusions. And really, that’s when you can start to see where the attack actually came from — the entry point and the vulnerability that let the attacker in.

Tactical Breakdown: How to Execute This Step

Transforming data means systematically analyzing and correlating your findings:

Execute your queries across the identified log sources
- Run searches in a logical order (process execution → network activity → persistence)
- Document findings as you go
Correlate events across sources
- Did the suspicious process execution (EDR) happen at the same time as the outbound connection (firewall)?
- Can you link the user account across multiple logs?
Filter false positives
- Is this behavior actually normal in your environment?
- Does the indicator match known legitimate software?
Build an evidence chain
- Create a timeline: User X → executed process Y → at timestamp Z → connected to IP A
- The stronger your correlation, the higher your confidence

This is where raw data becomes actionable intelligence.

⚡ Visual: Threat Hunting Flow (Mermaid Diagram)

flowchart TD
    A[Develop Hypothesis] --> B[Identify Evidence Sources]
    B --> C[Transform & Analyze Data]
    C --> D[Draw Conclusions]
    D --> E[Improve Detection Rules / Close Gaps]
    E --> A

Putting the Methodology Into Practice

This 4-step framework is designed to be repeatable across any threat hunt scenario. Whether you’re hunting for:

Credential dumping (Mimikatz, LSASS access)
Lateral movement (PsExec, RDP abuse)
Data exfiltration (unusual outbound transfers)
Persistence mechanisms (scheduled tasks, registry modifications)
Or unauthorized tool usage (like TOR browsers bypassing controls)

…the process remains the same.

The key is understanding that each step naturally expands into tactical actions:

Hypothesis guides what you’re looking for
Evidence sources tell you where to look
Transform & analyze shows you how to extract meaning
Refine detections ensures you’re better prepared next time. And this is where you actually close that entry point — the vulnerability — so it doesn’t happen again.

Want to see this methodology in action? Check out our practical walkthrough: Threat Hunt: Detecting TOR Usage on Your Network

In that guide, we apply this exact framework to a real-world scenario — showing you the specific queries, correlation techniques, and documentation practices that turn theory into results.

Recap: The Threat Hunting Formula

Threat hunting isn’t guesswork — it’s a structured, repeatable cycle:

Develop a hypothesis – define the attacker behavior you want to test.
Identify evidence sources – determine the right logs and fields to investigate.
Transform the data – analyze, correlate, and extract meaningful signals.
Refine detections – strengthen defenses and document learnings.

Each of these steps involves tactical execution — gathering intelligence, building queries, correlating findings, and implementing controls. The methodology provides the framework, the execution determines success. But here’s the thing — the real outcome is that you’ve figured out where the attack came from and how to close that door so it doesn’t happen again.

Visual: Thought Process

mindmap
  root((Threat Hunting Mindset))
    Hypothesis
      "Assume compromise"
      "Define expected attacker behavior"
    Evidence Sources
      "Sysmon / EDR"
      "Security Event Logs"
      "Firewall / Proxy"
      "Registry changes"
    Transform Data
      "Filter noise"
      "Correlate logs"
      "Spot anomalies"
    Outcome
      "Confirm or disprove hypothesis"
      "Develop detection rules"
      "Document lessons learned"

By repeating this process, hunts become purposeful, repeatable, and increasingly effective.

Threat Hunting SOC Operations Incident Response Threat Intelligence Behavioral Analysis