Certified: The GIAC GSOM Audio Course

In this episode, we focus on a reality every security team eventually faces, which is that no environment has perfect visibility and no team has endless time to build every detection from scratch. Even well-run programs discover gaps, such as missing coverage for a certain kind of attacker behavior, limited telemetry from a critical system, or noisy signals that make it hard to separate real threats from harmless activity. Community sourced resources exist because thousands of defenders across many organizations face similar problems, and many of them share what they learn in ways that others can reuse. For brand-new learners, the important point is that community resources are not a substitute for thinking, and they are not magic lists you can paste into a system to become safe. They are tools you can use to accelerate learning, broaden perspective, and strengthen detection when your own program has blind spots. When you use these resources carefully, you can close gaps faster, avoid repeating common mistakes, and improve your ability to spot attacker behavior before it becomes a major incident.

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.

To use community sourced resources well, you first need a clear idea of what a detection gap actually is, because not every discomfort is a gap and not every gap should be solved the same way. A detection gap is a place where your current monitoring and analysis cannot reliably answer an important security question. For example, you might not be able to tell whether a privileged account is being used unusually, whether a sensitive dataset is being accessed in unexpected ways, or whether a common attacker technique would produce signals you can see. Gaps can be about data collection, meaning the signals are not being recorded. They can be about analysis, meaning the signals exist but you do not have good ways to interpret them. They can also be about prioritization, meaning you have signals but the team cannot realistically review them or react to them due to volume or workflow constraints. This distinction matters because community resources can help most with analysis and interpretation, but they cannot fully fix missing data unless you change what you collect. When you can name the gap precisely, it becomes much easier to search for community guidance that fits and to avoid wasting time on resources that do not apply.

Community sourced resources come in a few broad forms, and it helps beginners to understand what each form is good for. Some resources describe attacker behaviors and techniques in a structured way, which helps you know what to hunt for and what to detect. Some resources provide example detection logic or analytic ideas that can be adapted to your environment. Some resources share incident patterns, lessons learned, and common pitfalls that help you recognize what matters first. Others provide collections of indicators, which can be useful in specific cases but must be handled carefully because they can become outdated quickly. There are also community playbooks and checklists that offer workflows for investigation and response, which can help you strengthen how you use detections, not just what you detect. The key is that community resources are varied in quality and intent, so you need to choose the right type for your gap. If your gap is lack of understanding, you may need conceptual resources, and if your gap is lack of analytic patterns, you may need detection idea resources. Matching resource type to gap type is the first step toward using community knowledge effectively.

A core skill is evaluating trust and fit, because community sourced does not automatically mean reliable, and even reliable information may not fit your environment. Trust depends on factors such as the credibility of the publisher, whether the resource is widely reviewed, whether it is maintained, and whether it includes enough context to understand its assumptions. Fit depends on whether the resource aligns with your data sources, your architecture, and your operational realities. A detection idea that assumes you have certain kinds of telemetry is not useful if you cannot observe those signals, and an analytic that works in one environment might be too noisy or too brittle in another. For beginners, a safe mental model is to treat community resources as starting points, not finished products, and to assume you must validate them before relying on them. Validation means checking whether the underlying behavior makes sense, whether the logic produces reasonable results, and whether it can be tuned to reduce false alarms. When you validate, you convert shared information into something defensible for your organization, which protects you from blindly adopting weak guidance.

Another important concept is that community resources are most powerful when they help you think in behaviors rather than in isolated indicators. Lists of specific indicators can be helpful during a known campaign, but they can also create a false sense of security because attackers can change surface details. Behavior-focused resources describe what attackers do to achieve goals, such as gaining credentials, escalating privileges, moving laterally, or accessing sensitive data, and those behaviors tend to leave detectable patterns even when specific values change. When you use behavior-based community guidance, you are less dependent on exact matches and more focused on recognizing meaningful sequences and relationships. This aligns naturally with threat hunting, where you start with a hypothesis about a behavior and then seek evidence that would confirm or deny it. Community knowledge can help you form better hypotheses by revealing which behaviors are common, which are high impact, and which are often missed. It can also help you understand what evidence tends to exist for those behaviors and where defenders often get fooled. This is a deeper and more durable use of community resources than simple indicator consumption.

A practical way to incorporate community resources is to treat them as part of your detection lifecycle, not as a one-time download. The detection lifecycle begins with identifying a gap, then choosing a resource that addresses that gap, then adapting and testing it, then deploying it in a controlled way, then monitoring performance and tuning, and finally documenting what you learned. Community resources can accelerate each step by offering initial logic, examples of expected patterns, or guidance on common false positives. The adaptation step is crucial because your environment has its own normal behavior, and what is suspicious elsewhere may be normal for you. Testing is equally crucial because you want to know what the detection produces, how noisy it is, and what evidence it provides when it triggers. Even if you are not implementing detections directly as a beginner, understanding this lifecycle helps you see why community resources are not plug-and-play. They must be integrated into a process that produces verified, explainable detection capability. When you treat them this way, community knowledge becomes a multiplier rather than a distraction.

Community sourced resources also help with prioritization, because they can tell you which gaps tend to matter most and which are common pathways for real attackers. In a world of infinite possible problems, you need ways to choose where to invest attention first. Community incident reports and shared defender experiences often highlight recurring themes, such as the importance of identity monitoring, the frequency of phishing-related credential misuse, or the value of focusing on privileged access pathways. This does not mean you blindly follow trends, because your environment may have unique risks, but it provides an evidence-informed starting point. It also helps you avoid spending months perfecting detections for rare, low-impact scenarios while leaving basic gaps that attackers exploit frequently. For beginners, it is helpful to remember that good prioritization is a form of defense, because it ensures limited time is spent on the highest-return improvements. Community guidance can strengthen your prioritization when it is used as a lens, not as a script. The goal is to align your detection growth with realistic adversary behaviors and practical defender experience.

There is also a people and process aspect to using community resources that is easy to overlook. When a team adopts community ideas, it must ensure the team understands them and can operate them. A detection that no one can explain will not be trusted, and a detection that triggers without an investigation path will create fatigue. Community resources often include context, such as why a pattern matters or what it could indicate, and that context should be carried into how the team documents and responds to the detection. This is where knowledge sharing inside your organization matters, because the value of community resources is multiplied when it becomes shared internal understanding. For beginners, think of it as learning a recipe and also learning why the recipe works, so you can adjust it when ingredients change. If you only copy the steps without understanding, you cannot troubleshoot when results are unexpected. When the team understands the logic, it can tune, investigate, and improve over time.

A major risk with community resources is over-collection and over-alerting, where you adopt too many ideas at once and overwhelm your own operations. It is tempting to take every shared analytic and deploy it because it feels like progress, but too many noisy signals can degrade your ability to respond to the signals that truly matter. A disciplined approach introduces new detection ideas gradually, measures their usefulness, and tunes them before expanding. You also consider operational cost, meaning how much time it takes to investigate an alert and how often it will trigger in your environment. If a detection triggers constantly and produces little value, it can reduce trust in the entire detection program. Community resources can help here too because they often discuss common sources of false positives and how to narrow scope, which can reduce noise. The key is to remember that detection capability is not just about having many alerts, but about having alerts that lead to meaningful action. A smaller set of high-confidence, well-understood detections is usually more powerful than a large set of confusing signals.

Community resources are also useful for strengthening how you validate detections and hunts, because they can provide expectations about what supporting evidence should look like. When you test a detection idea, you want to know whether the pattern you are seeing is consistent with known attacker behavior or whether it might be a benign workflow. Shared resources often describe typical sequences, related behaviors, and investigative pivots that help you interpret findings. This improves the defensibility of your conclusions because you are not inventing interpretation from scratch; you are using shared knowledge as a reference while still validating against your environment. It can also reveal gaps in your evidence chain, such as the inability to confirm a suspicious login with any host activity, which indicates missing visibility. By using community guidance as a validation aid, you turn it into a tool for improving both detection and analysis. This is especially valuable for new teams and new analysts because it accelerates learning and reduces the chance of misinterpreting signals. Over time, your organization’s own incident history becomes another internal resource that you can combine with community knowledge for even stronger conclusions.

In closing, community sourced resources are a powerful way to supplement detection capability gaps, but only when used with discipline, validation, and a clear understanding of your own environment. The first step is defining the gap precisely, because the best resource depends on whether you lack data, lack analytic patterns, or lack prioritization clarity. The next step is evaluating trust and fit, treating community ideas as starting points that must be adapted and tested rather than as automatic solutions. When you focus on behavior-based guidance, you gain durable detection and hunting hypotheses that remain useful even when attacker details change. When you integrate community resources into a lifecycle of gradual deployment, tuning, and documentation, you avoid overwhelming operations and instead build sustainable capability. Most importantly, when you combine community knowledge with your own evidence and baselines, you produce detections and conclusions that are defensible, understandable, and actionable. Used this way, community resources become a force multiplier that helps your S O C close gaps faster and respond with more confidence and clarity.