Certified: The GIAC GSOM Audio Course

In this episode, we’re going to take a skill that sounds abstract at first and make it feel concrete: turning an organizational use case into a clear set of data source requirements without getting stuck in vague language. Most beginners can describe what they want at a high level, like detect suspicious logins or spot unusual data access, but they struggle when asked what evidence would actually prove the behavior occurred. That gap matters because a Security Operations Center (S O C) cannot monitor what it cannot observe, and it cannot observe what it never collects. The exam tends to reward people who can bridge the gap between intent and evidence, because that is where monitoring becomes real instead of hypothetical. By the end, you should be able to hear a use case statement and quickly translate it into the kinds of events, fields, and context that must exist in telemetry for detection and investigation to work.

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 good use case is essentially a question you want to be able to answer confidently when something feels wrong, and the fastest way to turn that question into requirements is to restate it as an observable claim. If the use case is about unauthorized access, the claim might be that an identity accessed a system it normally does not access, or that the access happened under unusual conditions. If the use case is about privilege misuse, the claim might be that an account gained elevated permissions and then used them to perform a sensitive action. If the use case is about data exposure, the claim might be that a user accessed a sensitive repository and then moved an unusual amount of information out of the environment. Once you express the use case as an observable claim, you can ask what evidence would support or refute it, rather than getting lost in general security language. This approach keeps you moving quickly because it turns a broad goal into a testable statement.

To make requirements specific, you need to break the claim into the simplest building blocks of evidence, which are who, what, where, when, and how. Who is the identity involved, and that identity needs a stable identifier that can be linked across systems, not just a friendly display name. What is the action, which might be a login, a permission change, a file read, a configuration update, or a network connection, and the action must be represented as an event type you can search and correlate. Where is the target system or resource, which requires an asset identifier, a hostname, a service name, or a resource path, depending on the environment. When is the time of the action, which means you need reliable timestamps and time synchronization so sequences make sense. How includes context like source device, source network, authentication method, and success or failure state, because those details often separate normal from suspicious.

Once you know the evidence elements, the next acceleration step is to decide which systems are authoritative for each element, because not every system tells the full story. Identity systems are typically authoritative for authentication and role membership, but endpoints are often authoritative for what actually ran or changed on a device. Applications are authoritative for business actions inside the application, such as record changes or exports, while network telemetry can be authoritative for communication patterns between systems. Beginners sometimes expect one log source to carry everything, but real use cases often require combining multiple sources that each provide a piece of truth. Speed comes from accepting that reality early and mapping your evidence needs to the systems that naturally generate that evidence. This also prevents you from demanding impossible logs from systems that do not have them, which wastes time and leads to incomplete monitoring. When you identify authoritative sources, you can write cleaner requirements and prioritize collection more intelligently.

A fast translation method is to turn the use case into a short narrative of steps an attacker or mistake would have to take, then require data that confirms each step. For example, many access-related use cases follow a simple storyline: an identity attempts access, access is granted, a sensitive action occurs, and then evidence of movement or impact appears. Each step implies at least one data source and a few specific fields that must be present for correlation. Attempting access implies authentication logs with user identifier, source, outcome, and timestamp. A sensitive action implies application or system audit events that record the action type, the object affected, and the actor. Movement or impact implies either endpoint events that show new processes or changes, or network events that show unusual connections or data transfer patterns. This narrative technique is fast because it avoids overthinking and ensures you are collecting evidence for the whole chain, not just the first alert trigger.

Specific requirements also need field-level clarity, because an event that exists without the right fields is often unusable for investigations. If you want to detect suspicious logins, you do not just need login success events, you need fields that describe where the login came from, what device was used, and whether any risk indicators were present. If you want to detect privilege changes, you need fields that show the old privilege state and the new privilege state, not just that a change occurred. If you want to detect data access anomalies, you need fields that identify the dataset or object accessed and the action performed, not just a generic access event. Field-level clarity is what enables correlation, such as linking an identity event to an endpoint event by a shared user identifier, or linking an application action to a network transfer by a shared session or device identifier. When you move quickly, you should still insist on the minimum set of fields that make an event meaningful, because otherwise you will collect noise that cannot support decisions.

Speed also improves when you consciously separate detection requirements from investigation requirements, because they overlap but they are not identical. Detection often needs a smaller set of fields that can drive a reliable signal, such as unusual location, repeated failures, or an action that should be rare. Investigation needs richer context, such as which exact object was modified, what parameters changed, and what related actions happened before and after the event. If you try to satisfy every possible investigative need from day one, you can slow down collection planning and overwhelm the pipeline. If you focus only on minimal detection signals, you may generate alerts that cannot be confirmed quickly, which creates frustration and backlog. A balanced approach is to define the minimum viable fields for detection and then define a second tier of fields that dramatically improves investigation speed for the same use case. This layered thinking is fast because it keeps you moving toward a functional outcome while still planning for maturity and depth.

Another requirement area that beginners overlook is timing, because the usefulness of telemetry is shaped by how quickly it arrives and how long it remains available. Some use cases demand near-real-time visibility, such as detecting rapid credential abuse or active intrusion activity, while other use cases can tolerate delays, such as weekly reviews of permission drift. When you translate a use case into requirements, you should include how fresh the data needs to be for the response to matter, because delayed data can turn a response plan into a historical report. You also need to think about retention, because investigations often require looking back to understand what led up to an event, not just what happened at the moment of detection. Retention needs differ by use case, since some threats unfold slowly, and some compliance or audit needs require longer lookback windows. When you include timeliness and retention in your requirements, you make them operationally complete rather than narrowly technical.

Requirements should also specify the level of granularity needed, because use cases can fail when telemetry is too coarse to distinguish meaningful actions. For example, a generic event that says file accessed may not be enough if you need to know which file or which folder, especially when the use case is about sensitive information. A generic event that says configuration changed may not be enough if you need to know which setting changed, since some settings are harmless and others are security-critical. Granularity also applies to network visibility, where a broad summary might show that two systems communicated, but not whether the communication was unusual in volume or destination. You do not need to demand maximum granularity everywhere, but you do need enough detail to separate normal business operations from risky behavior. The fast way to decide granularity is to ask what ambiguity would remain if you only had the coarse event, because that ambiguity is what wastes time during triage and investigation. If a lack of detail would force you to guess, the requirement needs to be more specific.

Because organizations are messy, speed also comes from writing requirements in a way that survives variation across teams and systems, which means using stable concepts instead of fragile assumptions. Instead of requiring a specific log name, you can require a category of events, such as authentication outcomes, privilege changes, and high-impact administrative actions, as long as you also define the fields that must be present. Instead of requiring a particular location label, you can require a source attribute that supports location inference, such as network segment, device identifier, or known access channel. Instead of assuming every system reports the same user format, you can require a normalized user identifier that maps to the organization’s identity records. This approach keeps requirements portable as systems evolve, and it helps you communicate across technical boundaries without getting trapped in one platform’s vocabulary. The exam often tests this kind of thinking indirectly by presenting scenarios where you must choose what evidence would be needed, not which product feature to click. Portable requirements demonstrate that you understand the underlying monitoring need.

Fast translation also depends on prioritization, because you cannot collect everything at once, and you need a method to decide what to do first for each use case. A simple prioritization lens is impact and likelihood, where impact is how much damage the use case represents if it occurs, and likelihood is how often the behavior could realistically happen in your environment. High-impact, high-likelihood use cases deserve early collection investment because they prevent common and damaging outcomes, like credential abuse in widely used systems. High-impact, low-likelihood use cases may still be important, but they might be handled with lighter telemetry until the program matures. Low-impact use cases often belong later, especially if they require heavy data collection that could distract from more critical visibility gaps. When you prioritize at the use case level, you can then prioritize the data requirements inside the use case, starting with the minimal set that enables detection and basic investigation. This approach keeps momentum and prevents analysis paralysis.

A practical speed habit is to validate requirements against a quick mental test: could an analyst answer the use case question using only the data you required, without needing to call three other teams for basic facts. If the use case is about suspicious access, the analyst should be able to identify the user, identify the target, identify the source, and determine whether the access succeeded, all from collected telemetry plus basic context. If the use case is about a sensitive change, the analyst should be able to see who changed what, when it changed, and whether the change matches expected patterns or approvals. If your requirements do not support those basic answers, you are likely to produce alerts that cannot be handled efficiently, which makes the use case brittle. This mental test is fast because it focuses on the analyst’s first hour of work, where clarity matters most. It also encourages you to include essential enrichment needs, like asset criticality and role context, because those facts often decide whether something is urgent. Requirements that pass this test tend to be both exam-strong and operationally realistic.

You should also bake security and governance into requirements from the start, because telemetry collection can create risk if it is handled carelessly, especially when it touches sensitive business activity. Requirements should include access controls for who can view certain datasets, because operational logs can reveal personal activity, business strategies, or confidential transactions. They should include integrity expectations, such as whether logs are forwarded off-system quickly and whether collection pipelines are monitored for gaps. They should include data minimization where possible, meaning you collect the fields needed for security decisions but avoid capturing unnecessary content that increases privacy exposure. This does not slow you down if you treat it as a standard part of translation, because it becomes a checklist in your head rather than a separate project. It also improves trust with stakeholders, since business leaders are more likely to support telemetry collection when it is clearly bounded and responsibly managed. Strong requirements protect the monitoring mission without creating collateral risk.

As we close out, remember that turning use cases into data source requirements quickly is a thinking skill built on a few repeatable moves rather than on memorizing specific log types. You restate the use case as an observable claim, you break it into evidence elements like who and what and when, and you map those elements to authoritative systems that can produce trustworthy events. You then make the requirements practical by insisting on the minimum fields that enable correlation, by separating detection needs from investigation depth, and by adding timeliness and retention where they matter. You keep the process fast by using a simple narrative of steps, by prioritizing based on impact and likelihood, and by checking whether an analyst could answer the core question using only the data you required. Finally, you include secure handling and governance as part of the requirement, because telemetry is valuable precisely because it is sensitive. If you can do this translation cleanly, you can build monitoring that matches organizational reality and supports confident decisions instead of vague hopes.