Threat Hunting: Where to Start and How to Build It Into SOC Operations

A SOC lives by the rhythm of the alert queue: a rule fires, an analyst checks it, and closes it as a false positive or escalates it. This is necessary work, but by design it only reacts to what the detection system already knows how to recognize. If an attacker operates below the threshold that triggers rules, or uses legitimate administration tools, the alert queue stays silent.

Threat hunting exists to close exactly that gap: the analyst formulates a hypothesis about where activity might have gone unnoticed and checks it against telemetry directly, without waiting for an alert. It doesn't replace detection — it sits on top of it, finding what the rules don't yet cover and feeding those findings back into new rules.

What follows is a practical guide for teams planning to build proactive hunting into SOC operations: what you need before you start, how to formulate and close hypotheses, how to document results, and how to keep hunting from turning into a separate project that eats into shift capacity.

How Threat Hunting Differs From Alert Response

Alert response starts with an alert: a detection rule fires, and the analyst checks whether the threat is real. Threat hunting works the other way around — the analyst starts with a hypothesis along the lines of "there could be activity here that standard detection wouldn't have caught" and checks it directly against raw telemetry, regardless of whether anything actually fired.

The difference isn't in the tooling — both practices draw on the same telemetry — it's in the direction of travel. Alert response moves from signal to conclusion. Proactive hunting moves from knowledge of attacker behavior to a signal that might never have fired.

Proactive hunting doesn't replace triage or EDR incident investigation — the methodology for reading telemetry and building a timeline covered in "EDR Incident Investigation" is just as necessary for a hunter as for an analyst working through a ready-made alert. Only the reason for looking at the data changes.

What You Need Before You Start: Data, Retention, and Access

Threat hunting is impossible without telemetry to search through. Before planning your first hunt, you need three things in place: data sources, their retention, and analyst access to them. Without any one of these three, a hypothesis can't be tested — only stated.

Endpoint Telemetry

The primary source for most hypotheses is telemetry on processes, network connections, file activity, and command execution on workstations and servers. This is exactly what EDR collects, and without it a hunter is limited to logs that were never designed for investigation, only for system operation. What EDR is, how telemetry collection works, and why it isn't the same thing as antivirus is covered in "What Is EDR".

What matters for hunting isn't just that telemetry exists, but its depth: whether you can see a process's full command line and not just its name, whether you can see which process spawned which child, whether network connections are tied back to the initiating process. Without this level of detail, a hypothesis about a specific attack technique often can't be tested at all.

Mail and DNS Logs

Some persistence and command-and-control techniques show up not on the endpoint but at the mail and DNS-query level. A DNS query log helps you see whether a host contacted a domain that wasn't blocked automatically but looks suspicious in hindsight — say, a recently registered domain or one that mimics a legitimate brand. How this kind of logging works and what data it exposes is described in "How Secure DNS Works".

Mail logs, in turn, give you a history of delivered messages, attachments, and link clicks — useful for hypotheses about phishing that wasn't blocked at the gateway. A related topic is covered in "Secure DNS Against Phishing": it shows how the DNS layer and mail protection cover different parts of the same attack path, and why a hunter needs data from both sources.

Retention and Analyst Access

A hypothesis often needs data going back weeks, not just the last few hours — a persistence technique may have fired before anyone knew about it. If telemetry is retained for a week rather than months, some hypotheses simply can't be tested: the data you'd need to look at no longer exists. Retention period isn't a technical detail — it's a direct limit on which questions a SOC is even able to ask itself.

The second condition is access rights. An analyst planning a hunt needs to be able to build arbitrary queries against telemetry on their own, not request an export from another team. If every query requires approval and a wait, hunting loses its point: a hypothesis needs to be tested while it's still relevant, not two days after the request reaches whoever handles it.

How to Formulate and Test a Hypothesis

A good threat hunting hypothesis isn't built on a vague worry — "what if we've been breached" — but on a specific technique: exactly what an attacker might have done, what observable trace that technique leaves in available telemetry, and what query can find that trace. If any one of these three elements is missing — the technique isn't tied to a trace, or the trace can't be checked with the data on hand — the hypothesis needs to be reformulated before the hunt starts, not partway through.

The practical path to building a hypothesis looks like this:

1. Pick one technique, not an entire attack class. "Persistence via autorun" is a workable formulation; "ransomware attack" is not — it's too broad to translate into a concrete data query.
2. Define the observable trace. What exactly should be visible in telemetry if the technique was used: a specific registry key, a characteristic command line, a parent-child process sequence, a connection to a particular class of domains.
3. Formulate the telemetry query. It needs to be executable against the data you have today, not in theory once storage gets an upgrade.
4. Set the closure criterion in advance. What counts as confirmation, what counts as refutation, and how much data is enough to check before closing the hypothesis as unconfirmed. Without this criterion, a hunt risks running indefinitely in search of a trace that isn't there.
5. Run the query and work through every match the same way an analyst works through an alert: host, user, time, and surrounding-event context — not just the fact that something matched.

Hypotheses built around an adversary technique carry over between organizations and repeat in cycles — today you test persistence via autorun, and a quarter from now you return to the same technique with updated knowledge of its variants. Hypotheses built around vague worries start from zero every time.

How to Document the Results of a Hunt

A hunt that doesn't confirm an attacker's presence isn't wasted, as long as it gives the SOC one of three outcomes: a detection rule, an observation, or a process change. It's the documentation of the result that separates threat hunting from an analyst's one-off curiosity.

• Detection rule. If the hypothesis was confirmed even once, the observable trace is worth formalizing into a rule so that standard detection catches it next time, rather than requiring another dedicated hunt. This turns a one-off finding into permanent coverage.
• Observation. Not every hunt produces material for a rule — sometimes the result is that a telemetry source turned out to be incomplete, or the hypothesis was correct but the trace looks too much like legitimate activity for an automated rule. This kind of observation is worth writing down: it saves time for the next analyst who arrives at the same idea.
• Process change. Some threat hunting findings aren't about attackers at all — they're about infrastructure: a service logs the wrong thing, the EDR agent isn't deployed on some hosts, telemetry access is broader than it should be for certain roles. This is also a valid outcome of a hunt, and it's often worth more than a single indicator found along the way.

Every hunt deserves a short write-up: hypothesis, query, result, next steps. Without it, the knowledge lives only in one analyst's head and gets lost the moment the team rotates.

How to Build Hunting Into a SOC Shift Without Eating Into It

Threat hunting competes for time with the very same shift that's working the alert queue. Without a dedicated time slot, hunting either doesn't happen at all — because the alert queue always wins on priority — or happens haphazardly and never reaches a result.

The approach that works is to set a regular cycle with clear boundaries: a specific time slot in the shift schedule, a limited number of hypotheses per cycle, and a hard time-box for each one. A hypothesis that isn't tested within the allotted time gets pushed to the queue for the next cycle instead of dragging on indefinitely at the expense of the current shift.

The second element is rotation. Threat hunting shouldn't become one specialist's personal responsibility — otherwise it stops the moment they go on vacation or leave the team. Rotation spreads both the workload and the accumulated knowledge of which hypotheses have already been tested and with what result.

The third element is a source of hypotheses for the next cycle. Regular hunting quickly runs through the obvious assumptions. A reliable source of new hypotheses is prior incident investigations: whatever was found after the fact in one case is worth checking proactively across the whole infrastructure before it happens again.

How to Measure the Value of Threat Hunting

There's no set of precise numeric metrics that fits every organization equally, and leaning on someone else's numbers is pointless — what matters more is the qualitative picture inside your own SOC. Useful reference points:

• Share of hypotheses that reach an outcome — a rule, an observation, or a process change, rather than simply "checked and forgotten."
• Share of detection rules that originated from hunting, rather than from external feeds or incidents reviewed after the fact. This shows whether proactive hunting is actually staying ahead of reactive response.
• Time between a new technique surfacing publicly and testing the corresponding hypothesis in your own infrastructure — the shorter it is, the faster the SOC closes fresh blind spots.
• Recurrence of the same finding in future incidents. If a threat hunting finding turned into a rule, and that rule actually catches similar activity later on, the cycle is working as intended.

A qualitative statement of value holds up better than any percentage: investigating a single missed incident almost always costs a team more time and attention than the regular testing of a few hypotheses that could have prevented it.

Common Mistakes

• Hunting without data. The hypothesis is formulated, but the telemetry that could confirm or refute it simply isn't there — not in depth, not in retention. The result: time spent, no answer.
• Hunting for the sake of a report. The cycle happens because it's on the plan, not because the hypothesis is interesting and testable. These hunts get formally closed but produce neither rules nor observations.
• Hypotheses with no closure criterion. The analyst tests the same idea for weeks because they never decided in advance how much checking would count as enough. Either it eats up shift capacity, or the hypothesis gets abandoned halfway through with no conclusion.
• Mixing hunting with triage of current alerts. If an analyst gets pulled into the incoming queue during a dedicated hunting window, neither task gets finished.
• No record of the result. The finding lives in a chat thread or one person's head and gets lost at the next shift rotation.
• The same hypothesis, never updated. Attacker techniques evolve, and a hypothesis that was relevant a year ago may not reflect current variants of the same technique.

Where to Start in Week One

1. Check what you already have: endpoint telemetry, mail and DNS logs, their retention period, and whether analysts can query them independently.
2. Pick one persistence or lateral-movement technique that has already come up in your organization's past incidents — it's the most reliable source for a first hypothesis.
3. Define the observable trace and the telemetry query before you sit down at the search tool.
4. Set the hypothesis closure criterion and the time-box for testing it in advance.
5. Run the first cycle in a dedicated time window, shielded from the current alert queue.
6. Write down the result — even if the hypothesis wasn't confirmed.
7. If the trace is confirmed even once, turn it into a detection rule so you don't have to search for it by hand next time.

The first cycle is almost always slower than the ones that follow: the team is learning less about finding an attacker and more about formulating testable hypotheses and staying within the time-box. That's a normal part of adopting the practice, not a sign that it doesn't fit your SOC.

Practice on BI.ZONE Cybersecurity Labs Ranges

Threat hunting relies on the same ability to read telemetry as investigating a ready-made incident does — the only difference is what the analyst starts from: an alert or their own hypothesis. The BI.ZONE EDR Deployment and Administration course provides hands-on practice with exactly this telemetry on an isolated range: deploying the product, configuring protection policies, and working with the data that becomes the foundation for any hunter's hypothesis.

If your team deals with ready-made incidents more often than with proactive hunting, start with the article DFIR: How to Investigate an Incident Step by Step — many threat hunting findings turn into hypotheses precisely after working through a real investigation, not the other way around.