Optimizing Threat Hunting Operations: The CISOs Guide to Efficient and Effective Security Operations

Most threat hunting is reactive work wearing a proactive costume. Someone hands you an indicator, an IP, a hash, a Yara rule, a Sigma signature, and you go check whether that specific thing already sits in your environment. Matt, hosting the fourth and final part of LimaCharlie's CISO guide series, refused to let that pass as real hunting. His sharper point is the one most teams skip past: if you hunt across the same EDR or NDR telemetry that already drives your alerting, you are searching the exact source that was supposed to catch the thing in the first place. The honest version of the exercise starts with a harder question. If you cleared every alert out of your queue right now, are you done and secure, or are there things you should still be looking for?

The reactive hunt searches the source that already failed

Three habits keep hunting reactive, and they reinforce each other. It runs on indicators someone else defined, so it only ever finds known threats. It runs across limited visibility, usually the endpoint, so it inherits whatever blind spots the primary stack already has. And it leans on the same telemetry that feeds detection, which produces a contradiction Matt drew out plainly. If a piece of intelligence is so fresh that your team has to launch a hunt against it because your EDR software and its signatures have not kept up, that is not a win. It is a sign you should question how much you trust the technology you are paying for. There has to be an inflection point, he argued, where you ask why a tool is in place at all if you believe your analysts are routinely ahead of it.

The cost lands on the analysts doing the grunt work. They are already bombarded with alerts, already drowning in reactivity, already tired and overworked. Telling a team in that state to also write their own queries, parse unfamiliar data sources they have never normalized, and grind manually through large data sets looking for anomalies does not scale. It adds to burnout and quietly makes hunting a nonviable option. And the reflex Matt warned against, buying a product that hunts for you because your team cannot, just adds another layer onto the same cycle.

Hunt for what adversaries always do, not what one of them did once

The way out is to change what you hunt for. Adversaries develop new tactics constantly, Matt acknowledged, drawing on the threat intel he covers weekly on the Cybersecurity Defenders Podcast. But at certain phases of an attack the same old behaviors persist no matter how advanced the actor is. They want to blend into the environment, evade detection, take over legitimate accounts, abuse native protocols, and pass for legitimate applications. That predictable behavior is the opening. You cannot reliably hunt for tomorrow's hash, but you can hunt for the behaviors attackers cannot avoid.

This reframes threat intelligence from a lookup list into a lens. Instead of going to find a particular IP address, you notice that a lot of threat actors have migrated toward PowerShell, that some are writing malware in Rust, that VPN appliances are getting compromised, and you hunt those patterns instead. It also forces you off the endpoint. Plenty of actors never touch EDR or NDR at all. They live entirely in the cloud, slip between containers and microservices, or start at applications, appliances, and the perimeter and work their way in. Hunt only across endpoint telemetry and you arrive too late by design.

Normalize the data, then automate the hunt out of existence

Behavioral hunting across everything only works if everything is actually usable, and this is where Matt moved into the series' first live demo. LimaCharlie normalizes any source and treats it as a first class citizen, whether that is Slack audit logs, CloudTrail, Windows event logs, macOS unified logs, a random password manager, or EDR telemetry, all on equal footing. The deliberate non-goal is just as important. He is not ingesting everything into databases and then asking analysts to get good at hybrid SQL against raw tables. That, he said flatly, is not threat hunting. Normalization and enrichment, geolocation and threat intelligence among them, happen at the infrastructure layer so the analyst's only job is deciding what to look for. Every ingested event also carries a year of retention, which matters because hunting strips away time. A SOC analyst pivots on a timestamp, an alert at noon sends them to look at what happened at noon. A hunter looking for account abuse or native protocol abuse has no timestamp at all, so reaching back across a year of normalized data is what makes the hunt possible.

The real payoff is the loop he closed at the end. The LimaCharlie query console runs backward against history, his example listed Windows service installations, event ID 7045, a common persistence, privilege escalation, and lateral movement mechanism. Detection and response rules run forward, flagging the behavior as it happens. Replay connects them by testing a rule against a historical window before it goes live, so you tune out false positives in advance. Then comes the operating principle that gives the whole talk its spine. Run a query once and it is an ad hoc hunt. Run it twice, where the only thing that changed was when you asked, and you have proven you care about time, which means you will care when it happens again. At that point the hunt should stop being a query a human reruns and become a static detection the computer runs for you. Matt's favorite example is a rule that flags private repository clones and pushes from outside the US or Canada, IPs geolocated automatically. Not malicious on its face, maybe just someone logging in from vacation, but exactly the kind of question worth answering forever.

That is the argument underneath the demo. Reactive hunting does not scale because it depends on tips, on blind spots, and on tired people. The version that scales is the one where analysts ask good questions of normalized data once, then hand the repetition to the platform so they can move on to the findings that actually need a human.