Certified: The GIAC GSOM Audio Course

In this episode, we’re going to pause and fuse the last several topics into one clean mental system, because prioritizing, collecting, and enriching data sources only works when the pieces reinforce each other instead of drifting into separate projects. Beginners often learn these ideas as isolated steps, like first pick sources, then collect, then enrich, but real S O C work is more like a loop where each decision affects the next. If you prioritize poorly, you collect the wrong things and waste capacity. If you collect well but ignore enrichment, you generate alerts that are slow to triage and hard to trust. If you enrich without a plan, you can increase risk and complexity without improving decision speed. A spaced review is useful here because the exam expects you to reason about the whole chain, not just define vocabulary, and because blind spots usually appear in the seams between steps, not inside a single step. The goal for this review is to help you hold a simple, repeatable way of thinking in your head so you can design a telemetry program that sees meaningful behavior, supports fast decisions, and avoids the false confidence of collecting a lot of irrelevant data.

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 by locking in what prioritization really means in a S O C context, because it is not the same as choosing your favorite log sources. Prioritization is deciding which signals are most valuable for the organization’s highest risks, and that requires connecting data sources to the questions you must answer during an incident. The fastest way to do that is to think about what the business depends on, how access happens, where sensitive data lives, and what systems would cause major harm if compromised. Once you identify those critical areas, you rank data sources by how directly they reveal dangerous behaviors in those areas, such as credential abuse, privilege changes, execution of suspicious code, or unusual access to sensitive repositories. You also consider signal-to-noise, because a source that produces lots of routine events but rarely improves decisions can drown out more valuable signals. Good prioritization is defensible, meaning you can explain why you chose a source and what risk it reduces, rather than saying you chose it because it was easy. If you remember that prioritization is a risk-based decision about evidence, you will avoid the trap of collecting volume for its own sake.

Next, cement the idea that collection is a pipeline, because that single concept prevents many beginner mistakes. Collection begins at the source where events are generated, moves through capture and transport, and ends in storage and search where analysts can use it, and every stage can fail in ways that create blind spots. A pipeline must be reliable under normal conditions and under stress, such as during outages or during a surge of events caused by an incident. It must also be secure, because telemetry often contains sensitive operational information and because attackers can benefit by disabling or manipulating monitoring. That means you need protected transport, authentication between components, and access control so that collectors can read what they need without becoming a shortcut to administer systems. It also means you need observability of the pipeline, which is the ability to detect when a source goes silent, when delays grow, or when parsing errors rise. A pipeline you cannot monitor is a pipeline you cannot trust, and untrusted visibility is a form of blind spot because it creates false confidence. When you review collection, always ask yourself not only whether data is being sent, but whether it arrives intact, on time, and in a usable format.

Now lock in the role of enrichment, because enrichment is what makes monitoring faster and more decisive rather than slower and more confusing. Enrichment adds context like identity roles, privilege status, asset criticality, ownership, and network classification, which helps an analyst interpret an event without doing a manual lookup. It also supports correlation by normalizing identifiers so that events across systems can be connected into a single story, which is critical for understanding multi-step behaviors. Without enrichment, detections tend to be blunt, relying on simple thresholds that generate noise, and investigations tend to be slow because basic facts are missing. With enrichment, detections can be more precise, such as applying stricter expectations to privileged accounts or to critical systems, and triage can be faster because the alert already includes the context needed to judge impact. Enrichment also has risks, because adding context can increase data sensitivity and can introduce errors if the mappings are wrong or stale. This is why enrichment must be governed and validated, with clear controls over who can change it and checks for missing or inconsistent tags. In a spaced review, remember that enrichment is not extra decoration, it is the mechanism that turns events into meaning.

To avoid blind spots, you need a way to reason about coverage, because coverage is not the same as the number of data sources you collect. Coverage is the ability to observe important behaviors across the systems that matter, with enough context to recognize and investigate those behaviors. A blind spot can be obvious, like having no visibility into authentication activity, or it can be subtle, like collecting authentication events but lacking the fields needed to distinguish normal remote access from suspicious access. Another subtle blind spot is collecting events but failing to normalize key identifiers, which makes correlation difficult and hides patterns that span systems. Blind spots also appear when pipelines are fragile, such as when a parser silently breaks after an update and the team continues to believe the data is complete. A strong way to review coverage is to think in attacker behaviors, such as access, privilege, execution, movement, and data access, and ask whether you can observe those behaviors on your critical systems. If you cannot, the gap is not just missing telemetry, it is missing evidence of a behavior category that could occur. This behavioral view keeps coverage assessment stable even as the environment changes.

A simple and fast method to connect prioritization, collection, and enrichment is to start from a use case and translate it into evidence requirements, then verify you can satisfy those requirements end to end. For any use case, identify what you must prove, such as who performed an action, what action occurred, where it occurred, when it occurred, and what changed as a result. Then identify which systems are authoritative for those facts, which tells you what sources to prioritize. Next, ensure the pipeline can deliver those events reliably and securely, which tells you what collection design choices and health checks matter. Finally, define the enrichment that makes the evidence usable, such as role mapping, asset criticality, and network classification, which tells you what context must be attached for fast triage. This method is fast because it forces you to think about outcomes rather than collecting random logs, and it is strong because it reveals where gaps are hiding. If you cannot answer a basic investigative question without calling other teams for fundamental facts, then the use case is not fully supported. That is how you detect blind spots in the seams between steps.

Another key spaced-review point is the difference between detection value and investigation value, because many telemetry decisions fail when they focus on only one of these. Detection value is whether a signal can reliably tell you something might be wrong, while investigation value is whether you can confirm what happened and decide what to do. Some sources are great at early detection but weak at explanation, while others are great at explanation but too heavy or too slow for early warning. A mature telemetry program tries to cover both by collecting enough for reliable detection and enough for confident investigation, especially for the highest-risk use cases. This is also where tiering becomes useful, because you might keep high-value investigation data for longer or store it in a different way than routine telemetry. The danger is building a system that generates alerts that cannot be investigated, because that creates backlog and distrust, or building a system that stores deep detail but fails to detect promptly, because that turns security into a forensic exercise after damage is done. When you review, ask whether your choices support both seeing and proving, because both are necessary for effective response. This dual lens helps you prioritize sensible sources and avoid collecting data that only looks useful.

Pipeline health and data quality are also part of avoiding blind spots, because incomplete or inconsistent data can silently break your ability to detect patterns. Data quality includes completeness, meaning the expected event types are arriving, and correctness, meaning the fields reflect what you think they reflect. It includes time consistency, because investigations depend on event order, and it includes normalization consistency, because correlation depends on stable identifiers. Quality issues often appear as sudden changes in alert volume, strange gaps in timelines, or an increase in parsing failures, and those should trigger pipeline investigation rather than just alert tuning. A key habit is to treat missing data as an incident for the monitoring system itself, because losing visibility is a high-impact event even if no attacker is involved. Another habit is to use the same evidence-based thinking for pipeline reliability that you use for threat detection, meaning you watch for signs that the system is drifting away from expected behavior. When you keep quality in focus, you reduce the risk of being blind without realizing it, which is one of the worst outcomes for a S O C. In spaced review terms, remember that good monitoring is not only about what you collect, but about how trustworthy what you collect remains over time.

Enrichment validation is its own blind-spot prevention tool, because wrong context can mislead decisions even when raw data is correct. If an account is incorrectly tagged as non-privileged, risky activity might be downplayed, and if a critical database is incorrectly tagged as low impact, response urgency might be reduced. If network ranges are outdated, legitimate internal traffic might be misclassified as external, creating noise that hides real issues. Validation can include checks for missing tags, checks for unusual shifts in criticality labels, and checks for identity attributes that suddenly change without a known reason. It also includes understanding the freshness of enrichment sources, because some context changes quickly, like role assignments, while other context changes slowly, like asset ownership. A good enrichment program is careful about what it attaches permanently and what it looks up dynamically, because stale context can create long-lived confusion. For beginners, the key is to treat enrichment as part of the monitoring system’s integrity, not as a convenience feature. When enrichment is accurate and well governed, it speeds triage, improves detection precision, and reduces blind spots created by misinterpretation.

Finally, a spaced-review way to keep everything aligned is to adopt a continuous improvement loop, because telemetry programs cannot be perfect at first and they do not stay perfect without care. Every investigation teaches you which sources were helpful, which sources were missing, and which context would have made decisions faster. Every major system change teaches you where pipelines break and which parsers or mappings are fragile. Every tuning cycle teaches you whether noise is caused by poor detection logic or by missing enrichment that would separate normal from abnormal. The loop is simple: prioritize based on current risk and use cases, collect through a reliable pipeline, enrich for fast decisions, validate quality, and then refine based on what you learn. This loop is how you gradually eliminate blind spots without drowning in complexity. It also keeps your work defensible because each change is tied to a need revealed by evidence, not to random expansion. When you can describe this loop clearly, you are demonstrating the kind of reasoning the exam expects from someone who understands S O C operations.

As we close, hold onto one integrated takeaway: prioritization chooses the evidence that matters, collection delivers that evidence reliably and securely, and enrichment turns that evidence into fast, confident decisions. Blind spots appear when any link is weak, such as when priorities ignore critical business workflows, when pipelines fail silently, when identifiers are inconsistent, or when context is missing or wrong. The best defense against blind spots is to think in behaviors and use cases, define what must be observable, and then verify that your end-to-end system can support detection and investigation with trustworthy data. When you treat telemetry as a living program with validation and feedback, you avoid the false confidence that comes from volume alone. For the exam and for practical thinking, remember that effective S O C visibility is not about collecting everything, it is about collecting the right things, delivering them reliably, and enriching them so that monitoring becomes decisively faster and more accurate.