Certified: The GIAC GSOM Audio Course

In this episode, we’re going to focus on alert classification, which is the practice of labeling and organizing alerts in a consistent way so that a S O C can move faster and make fewer mistakes. Beginners sometimes treat classification as administrative busywork, like a filing system that exists only for reports, but classification is actually a speed tool. When alerts are classified consistently, analysts can triage faster because they know what kind of problem they are looking at, teams can route work to the right owners without confusion, and investigation handoffs become cleaner because everyone shares the same understanding of what the alert represents. When classification is inconsistent, the opposite happens: the same alert type is handled differently by different people, routing becomes guesswork, and the S O C spends more time coordinating than investigating. The exam expects you to understand these operational mechanics because a S O C is not just detection logic, it is a human system that must coordinate under uncertainty. By the end, you should be able to explain what classification is, what a useful classification scheme looks like, and how consistent classification reduces backlog and increases the quality of investigations.

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.

Start with a clear definition: classification is assigning a structured set of labels to an alert that describe what the alert is about in ways that support action. Those labels might describe the suspected behavior category, the affected asset type, the identity type, the environment, and the urgency or impact level. The point is not to create dozens of tags that no one uses, but to capture the minimum set of facts that help an analyst immediately place the alert into the right mental bucket. For example, there is a difference between an alert about suspicious authentication behavior and an alert about sensitive data access, even if both involve the same user. There is also a difference between an alert about a production system and an alert about a test system, even if the technical pattern is identical. Classification makes these distinctions explicit, which reduces time spent interpreting and debating. A good classification also includes an outcome classification, meaning how the alert was resolved, such as confirmed malicious, confirmed benign, or unable to determine with available evidence. That outcome classification is what enables learning and tuning later. For beginners, the key is to see classification as a way of turning raw alerts into manageable work categories, not as a reporting chore.

Classification speeds triage because it creates predictability, and predictability is what allows analysts to move quickly without re-deriving the meaning of every alert from scratch. When a new alert arrives, triage requires a quick understanding of what the alert implies and what a reasonable first validation step should be. If the alert is classified clearly as suspicious authentication, the analyst knows to check identity context, login patterns, recent password or role changes, and related access attempts. If the alert is classified as endpoint execution anomaly, the analyst knows to look for process lineage, recent downloads, persistence changes, and peer activity from the same host. If the alert is classified as data access anomaly, the analyst knows to confirm the dataset, access rights, and whether the access aligns with the user’s role and current business activity. Classification creates these mental shortcuts, which reduce time and reduce missed steps. It also helps new analysts learn faster because they can build consistent play patterns for each category. For the exam, this shows an understanding of how operations scale beyond individual skill.

Routing is the next speed benefit, because many alerts must be handled by the right people, not just by whoever happens to see them first. Routing can mean sending an alert to a specialist team, escalating to incident response, or involving a system owner who understands normal behavior for a service. Classification helps routing by making it clear which domain the alert belongs to, such as identity, endpoint, network, or application, and by indicating what kind of asset or business service is affected. When classification includes asset ownership and environment tags, it becomes much easier to determine who needs to be involved and how urgent the communication should be. Without classification, routing becomes a manual investigation in itself, which wastes time during critical moments. Misrouting is also dangerous because it can delay response, and delays are often what turn a containable incident into a major one. Consistent classification reduces misrouting by standardizing the decision logic for where an alert should go. For beginners, it is useful to remember that routing is a coordination problem, and classification is the data that makes coordination efficient.

Investigation handoffs benefit from classification because handoffs fail most often when the receiving person cannot quickly understand what has already been done and what the alert represents. A handoff might occur between shifts, between analysts with different specialties, or between the S O C and a broader incident response function. If the alert is classified consistently, the handoff can include a shared set of labels that immediately communicate scope, suspected behavior, affected assets, and current status. That reduces the risk that the next person repeats work or misses key context because they interpret the alert differently. Classification also supports case grouping, where multiple alerts that share key classifications can be combined into a single incident view, such as several alerts all tied to one identity, one host, or one business service. Grouping reduces noise and helps reveal patterns that would be missed if alerts were handled separately. When classification is consistent, grouping rules become clearer and more reliable. For beginners, the main point is that handoffs require shared language, and classification is the structured language that makes that possible.

A useful classification scheme should be simple enough to use consistently and rich enough to support decisions, which is a balance many programs get wrong. If the scheme has too few categories, everything becomes a generic bucket and classification loses value. If the scheme has too many categories, analysts will apply them inconsistently and the scheme will collapse into confusion. A practical approach is to classify along a few stable dimensions that matter operationally, such as behavior category, asset criticality, identity privilege, environment, and confidence level. Behavior category should align with observable patterns, like authentication anomalies, privilege changes, suspicious execution, unusual network connections, or sensitive data access. Asset and identity dimensions should reflect business context, like whether the system is production and whether the account is privileged. Confidence should reflect evidence quality, which can guide how quickly and how aggressively the S O C responds. These dimensions are stable because they apply across many systems and do not depend on vendor-specific terminology. For beginners, it is important to see classification as a structured summary of what matters, not as a detailed technical description of every field in the alert.

Classification also supports reporting and improvement, but the key is that it supports improvement only when it is applied consistently and tied to outcomes. If you can reliably count how many alerts were in each category and how many were true positives, you can identify which detections need tuning and which areas of coverage are producing the most meaningful signals. If you can track time to triage and time to resolution by classification, you can find categories that are slow because they lack enrichment or because they require clearer procedures. If you can track which classifications lead to escalations, you can ensure the right alerts are reaching incident response at the right times. Outcome classification is especially important because it connects detection design to reality, showing whether an alert category is mostly noise or mostly value. Without outcome tags, tuning becomes opinion-based rather than evidence-based, because you cannot prove which alerts are worth improving. For beginners, it helps to recognize that classification is how the S O C learns as a system, because learning requires organizing experience into comparable groups. Consistent classification is what makes that learning trustworthy.

To classify consistently, the S O C needs clear definitions and guardrails, because humans naturally interpret ambiguous labels differently. Definitions should explain what qualifies for each behavior category and what does not, so analysts do not make subjective decisions that vary by person and by day. Guardrails can include default classifications that are automatically applied based on detection logic, with room for analysts to adjust when evidence suggests a different category. This is important because many alerts start as hypotheses, and investigation might reveal that the initial classification was wrong, such as a suspected brute force event that turns out to be a misconfigured service. Updating classification based on investigation outcomes is healthy as long as the changes are controlled and documented, because it improves accuracy over time. Training also matters because analysts need to practice applying the scheme, especially in edge cases, and those discussions help refine definitions. For beginners, it is useful to see classification as part of operational discipline, like a shared vocabulary that must be taught and maintained. When definitions are clear and consistently applied, the S O C reduces friction and speeds decision-making.

Another important concept is that classification should integrate with enrichment, because many classification fields depend on context rather than on raw events. Asset criticality, environment, and ownership are often enrichment fields derived from inventories and business records. Identity privilege and role are identity enrichment fields that require reliable mappings. Network zone and access channel are often derived from network context and known ranges. If enrichment is missing or wrong, classification will also be wrong, which can misroute alerts or misprioritize work. This connection means classification schemes should be designed with available enrichment in mind, and enrichment programs should prioritize the context needed for classification accuracy. For example, if routing depends on knowing the business service owner, then ownership tags must be accurate and consistently applied. If prioritization depends on knowing whether an account is privileged, then privilege indicators must be reliable. For beginners, the key is to see classification not as isolated labels, but as the operational use of enrichment to organize work. Strong classification is one of the clearest ways to turn enrichment investment into measurable speed gains.

Classification also supports escalation decisions by making it easier to recognize when a pattern is expanding beyond a single alert type. If multiple alerts share a common identity, host, or service classification, they may represent a broader incident that requires coordination rather than isolated handling. For example, an authentication anomaly classification followed by an endpoint execution classification on the same host suggests a multi-stage compromise pattern. A privilege change classification followed by unusual data access classification suggests that an attacker may be moving toward a goal. When classification is consistent, these patterns stand out more clearly and can trigger escalation criteria that are based on combined evidence rather than on a single alert. This is one of the biggest operational advantages of classification, because it helps the S O C see the bigger story faster. It also reduces the chance that separate analysts handle related alerts independently and miss the connection. For beginners, it is helpful to remember that attackers rarely generate only one alert, and classification is one way to connect the dots. A good scheme supports both individual triage and incident-level recognition.

As we conclude, remember that consistent classification is a speed multiplier because it turns a stream of alerts into organized work that can be triaged, routed, and handed off efficiently. Classification assigns structured labels that describe suspected behavior and key context, which helps analysts quickly know what they are dealing with and what validation path makes sense. It improves routing by making ownership and domain clear, and it improves handoffs by giving everyone shared language about scope and status. A useful classification scheme balances simplicity and usefulness by focusing on stable dimensions like behavior category, asset criticality, identity privilege, environment, and confidence. Consistency requires clear definitions, guardrails, and the willingness to update classification when investigations reveal a different reality, all while documenting changes. Classification depends on enrichment, so accurate context is essential for accurate labels and effective routing. Finally, classification enables learning by tying alert categories to outcomes, which supports tuning and program improvement over time. If you can explain classification as an operational tool for speed and coordination, you are thinking in the way the exam expects and in the way a real S O C must operate to stay effective.