In this episode, we’re going to take the idea of defending a network and push it one step further, into the mindset of looking for trouble before trouble loudly announces itself. A lot of beginners think security is mostly about waiting for an alert and then responding, but real attackers often move quietly, reuse access they already have, and avoid obvious alarms for as long as they can. Threat hunting is the practice of actively searching for signs of malicious behavior that might not trigger standard alerts, and intelligence is the practice of using information to make those searches smarter and more targeted. When you combine hunting and intelligence, you stop being purely reactive and start becoming curious in a structured, evidence-driven way. The goal is not to chase every rumor or to hunt randomly across all data, but to learn how to choose good questions, gather relevant signals, and turn weak clues into clear answers. By the end, you should understand how internal sources, Open Source Intelligence (O S I N T), dark web visibility, and Information Sharing and Analysis Center (I S A C) communities fit together into one practical defensive approach.
Before we continue, a quick note: this audio course is a companion to our course companion books. The first book is about the exam and provides detailed information on how to pass it best. The second book is a Kindle-only eBook that contains 1,000 flashcards that can be used on your mobile device or Kindle. Check them both out at Cyber Author dot me, in the Bare Metal Study Guides Series.
A solid way to approach threat hunting is to define what makes it different from ordinary monitoring and why it matters even when you already have alerts. Monitoring is often rule-driven, meaning the system looks for patterns someone anticipated and encoded into detections. Threat hunting is hypothesis-driven, meaning you start with an idea about how an attacker might behave, then you look for evidence of that behavior across your environment. That difference matters because attackers constantly change their tactics, and some of their actions blend into normal activity unless you look for subtle combinations of signals. Hunting also matters because organizations often have blind spots, like devices that aren’t fully monitored, cloud services with limited logging, or legacy systems that can’t run modern security agents. A hunter’s job is to work within those constraints and still look for indications that something is off, such as unusual authentication patterns, odd administrative actions, or data access that doesn’t fit a user’s role. Beginners sometimes worry that hunting sounds like guessing, but it is actually disciplined investigation, where you start with a reasonable suspicion and test it with evidence. The best hunts are repeatable, measurable, and grounded in the reality of what your environment can reveal.
Internal sources are the foundation of threat hunting because they tell you what is happening inside your own walls, and nothing outside your environment can replace that. Internal sources include identity and authentication events, endpoint and server logs, network flow records, application logs, cloud activity logs, and even asset inventory and configuration data. These sources matter because attackers must interact with your systems to achieve goals, and interaction creates traces, even when the attacker tries to be quiet. A beginner misconception is that internal sources are only useful after you already know there is an incident, but in hunting, internal sources are how you discover the incident in the first place. Another misconception is thinking you need perfect data to hunt, when in reality you can hunt with imperfect data as long as you understand the limitations and choose hypotheses that match what you can observe. Internal sources also include “business truth,” like who is on vacation, which servers are critical, and what normal maintenance windows look like, because attackers often hide behind normal change activity. A good hunter treats internal sources as a rich, imperfect record of behavior and learns to read that record carefully.
The next step is learning how to turn internal data into a usable hunting strategy, because raw logs are not a plan by themselves. A beginner-friendly hunting approach starts with baselines, meaning you learn normal patterns for key behaviors like logins, privilege changes, remote administration, and data movement. Once you have a baseline, you can look for deviations that fit attacker goals, such as a user logging in from a new location and then performing administrative actions they never do. Another useful method is focusing on high-value behaviors rather than high-volume events, because many attacks hinge on specific transitions, like gaining privileged access, creating persistence, or accessing sensitive repositories. Hunters also pay attention to relationships, not just events, because one suspicious event can be benign, but a chain of related events can reveal intent. For example, a single failed login might be normal, but repeated failures across multiple accounts followed by a successful login and a token use can suggest credential testing and eventual compromise. Internal sources become powerful when you’re not just collecting them, but connecting them into sequences that reflect how attackers operate. The skill is to ask questions that can be answered with your data, then to interpret results honestly.
Open Source Intelligence, or O S I N T, is the next big piece, and it helps you decide which hunting questions are worth asking. O S I N T is information that is publicly available, such as security research reports, vulnerability disclosures, technical blogs, public code repositories, social media posts, breach announcements, and attacker infrastructure observations shared by researchers. The value of O S I N T is that it expands your awareness beyond your own environment, showing you what kinds of attacks are currently common, what vulnerabilities are being exploited, and what behaviors defenders are observing in the wild. Beginners sometimes think O S I N T is just reading headlines, but high-quality O S I N T is more like pattern recognition and context building. It helps you understand whether a threat is theoretical or actively exploited, and it helps you anticipate what an attacker might try next. It also helps you interpret internal signals, because a weird pattern in your logs might match a newly reported technique, turning a vague suspicion into a focused hunt. Used well, O S I N T doesn’t replace internal evidence, it improves the questions you ask of your internal evidence.
A practical way to incorporate O S I N T into hunting is to treat it as a source of hypotheses rather than a source of conclusions. If researchers report that a certain type of phishing campaign often leads to token theft, you can hunt internally for unusual token usage patterns, especially following suspicious login activity. If a new vulnerability is being exploited broadly, you can hunt for scan patterns, abnormal error responses, or process activity that aligns with exploitation attempts, even before you confirm patching status everywhere. If attackers are reported to be using a particular persistence method, you can hunt for that persistence artifact across systems that are most likely to be targeted. Beginners sometimes overreact to O S I N T, feeling like every new report means immediate crisis, but the mature approach is to map external information to your environment’s exposure. Do you run the affected technology, do you expose it in ways attackers can reach, and do you have internal signals that could reveal attempts? When you answer those questions, O S I N T becomes a disciplined input to prioritization, not a constant source of panic. That discipline is what makes intelligence useful rather than overwhelming.
The dark web is often discussed as if it is a magical place where all secrets are sold in plain view, and beginners should approach that idea carefully. The dark web is a part of the internet that is accessed through specialized networks and is often used for privacy and anonymity, and that anonymity is sometimes used by criminals to trade stolen data, access credentials, tools, and services. The defensive value of dark web visibility is that it can provide early warning that your organization’s data, credentials, or internal access is being marketed or discussed. The limitation is that not everything claimed is real, not everything is complete, and not everything is visible to defenders, because many criminal communities are private and trust-based. Beginners sometimes assume dark web monitoring is a guarantee of detection, but it is better understood as a potential signal source, like a tip line that sometimes provides valuable leads. When dark web signals are real, they can be extremely important, because a leaked credential set or a stolen session token can enable attacks that look like legitimate access. The goal is to treat dark web information as a prompt to investigate internally, not as proof by itself.
When you do receive a credible dark web signal, the defensive mindset should immediately shift to verification and containment, because time matters once secrets are exposed. If the signal suggests credentials are available, you want to determine whether those credentials are still valid, whether they were used, and what they can access. Internal sources become essential here, because you look for unusual logins, unusual token issuance, anomalous access patterns, and privilege changes tied to the affected accounts. You also think about scope, meaning whether the leak is isolated to a single user or suggests broader compromise, such as a password reuse pattern or a compromised third-party system. Beginners sometimes focus on the embarrassment of a leak, but defenders focus on the operational reality: leaked secrets are a pathway, and pathways must be closed quickly through resets, revocation, and stronger authentication where feasible. Dark web information is most useful when it triggers targeted hunts and targeted remediation, because it gives you a specific set of accounts, domains, or data types to investigate. Even if the signal turns out to be incomplete, the investigation often reveals gaps in monitoring or credential hygiene that are worth fixing. In this way, dark web visibility becomes a catalyst for improving security posture, not just a source of fear.
Information Sharing and Analysis Center, or I S A C, communities add another type of intelligence that sits between public O S I N T and private internal data. An I S A C is typically a sector-focused sharing community where organizations exchange threat information, trends, and defensive lessons with others who face similar risks. The key value is relevance, because what matters to one industry might not matter to another, and I S A C sharing tends to focus on threats that are actually impacting that sector. Beginners sometimes think sharing sounds risky, but these communities often operate with clear rules for what can be shared, how sensitive details are handled, and how to distribute actionable guidance without exposing private internal information. Another benefit is speed, because sector peers can observe attack patterns early and warn each other before those patterns become widely public. I S A C information can include tactics being used, systems being targeted, and recommended defensive checks, which makes it directly useful for hunting. In a practical sense, I S A C participation helps you stop feeling alone, because you learn what is happening to peers and how they are responding. For defenders, that means better prioritization and fewer surprises.
To use I S A C intelligence effectively, you want to translate shared insights into concrete internal questions rather than treating them as general awareness. If an I S A C reports a surge in attacks abusing remote access pathways, you can examine your internal logs for unusual remote logins, new device enrollments, or repeated access attempts against critical systems. If the community highlights a pattern of compromised vendor credentials, you can hunt for unusual access from vendor accounts, unexpected access times, or sudden configuration changes in systems those vendors manage. If the sharing includes indicators like suspicious domains or addresses, those can be used as pivot points in your monitoring data to see whether your environment has touched them. Beginners should understand that not every shared indicator will match your environment, and that is normal, because intelligence is about probabilities, not certainty. The goal is to integrate I S A C insights into a steady cycle: learn what peers are seeing, decide whether your exposure matches, hunt internally for corresponding traces, and adjust defenses accordingly. When you treat sharing as an input to your own evidence-based work, it becomes a force multiplier rather than a distraction. That’s how intelligence becomes operational rather than merely informational.
Threat hunting also requires a clear understanding of common failure modes, because a hunt can fail even when the organization has good intentions. One failure mode is hunting without a hypothesis, where analysts wander through logs until they find something odd, which often produces wasted effort and inconsistent results. Another failure mode is hunting with a hypothesis that the environment cannot support, such as trying to find specific endpoint behaviors when endpoint telemetry is missing or incomplete. A third failure mode is failing to document what was done and what was learned, which causes repeated work and prevents improvement of detection content. Beginners sometimes think hunting is a heroic activity performed by experts, but in practice, strong hunting is structured, repeatable, and designed to improve the system over time. Even when a hunt finds nothing, it should produce value by confirming a control is working, validating a baseline, or revealing a visibility gap. A mature hunting program turns discoveries into better detections, better configurations, and better training for responders, so the next time the pattern appears, it triggers an alert instead of requiring a hunt. That is how hunting evolves from artisanal work into organizational capability.
Another crucial idea is that threat hunting should be tied to business risk rather than only technical curiosity, because time is limited and the highest-value hunts focus on the highest-impact threats. That means you prioritize hunts related to critical assets, privileged identities, sensitive data repositories, and high-exposure services. It also means you incorporate vulnerability and configuration context, because a hunt for exploitation attempts makes more sense when the targeted technology exists and is exposed. Beginners can think of this like medical screening: you don’t run every test on every person every day, you choose tests based on risk factors and symptoms. Similarly, hunting uses intelligence to identify likely threats and internal context to identify likely targets. When you combine those, you create hunts that have a higher chance of finding meaningful issues and a clearer path to mitigation if you do find them. This approach also reduces alert fatigue in a different way, because well-designed hunts can validate what alerts are missing and help you build new, high-confidence detections. Over time, the hunt program becomes a feedback loop that strengthens monitoring and reduces surprises. That is what it means to apply hunting and intelligence as a disciplined practice.
The relationship between hunting and intelligence becomes clearest when you think of intelligence as the map and hunting as the search party. Intelligence suggests where attackers might travel, what tools they might use, and what targets they prefer, while hunting checks whether those paths appear in your environment. Internal sources are where you validate reality, because they contain the evidence of what actually occurred. O S I N T broadens the map with public patterns and vulnerabilities, dark web signals provide occasional high-impact tips about exposure of secrets and access, and I S A C communities provide sector-specific insight that is often more directly relevant than broad internet chatter. Beginners sometimes treat these sources as competing, but they are complementary because each fills a different gap. The key is to maintain a healthy skepticism toward each source while still extracting its value, because intelligence can be incomplete or wrong and internal logs can be missing or misparsed. A defender’s strength is in connecting imperfect information into a coherent investigation approach that avoids overconfidence. That combination of curiosity and discipline is what makes threat hunting effective.
As you build skill, you’ll notice that the best hunting questions are phrased in terms of behaviors and relationships rather than in terms of specific malware names. Attackers can rename tools, change file hashes, and adjust infrastructure quickly, but many attacker goals require similar behaviors, such as credential access, privilege escalation, persistence, lateral movement, and data access. Internal sources can reveal those behaviors through patterns like unusual login sequences, unexpected administrative activity, odd process behavior on servers, and unusual data flows. Intelligence helps you choose which behaviors to prioritize and what variations to expect, so you don’t hunt too narrowly. Beginners sometimes think the purpose is to catch a specific threat actor, but the more practical purpose is to detect suspicious behavior that indicates compromise, regardless of who is behind it. When a hunt finds a pattern, the next step is often to decide whether it is benign, misconfigured, or malicious, and each outcome leads to improvement. Benign discoveries improve baselines, misconfiguration discoveries improve hardening, and malicious discoveries trigger incident response. In all cases, the environment becomes more defensible because you learned something real about how it behaves.
To conclude, applying threat hunting and intelligence is about building a proactive, evidence-driven habit that makes attackers’ quiet work harder to sustain. Internal sources provide the ground truth of behavior inside your environment, and hunting uses that truth to test hypotheses about compromise that might not generate obvious alerts. O S I N T supplies broad awareness of what is happening in the world and helps you choose relevant hunting questions, while dark web signals can provide urgent clues about leaked access and exposed data that demand verification and containment. I S A C communities add industry-relevant sharing that often turns broad threat noise into practical, sector-specific guidance that you can translate into targeted hunts. The mature approach is to treat intelligence as guidance, not as certainty, and to treat hunting as structured investigation, not as random searching. When you do that consistently, you reduce blind spots, improve your detections, and build confidence that your environment is not only monitored, but understood. That understanding is what separates reactive defense from resilient defense, because it allows you to find the quiet attacker before the attacker decides to become loud.