Certified: The GIAC GSLC Audio Course

In this episode, we take the raw materials of security operations, logs, events, and telemetry, and turn them into something that actually helps: use cases that produce actionable detection. Most organizations have plenty of data, and many even have sophisticated platforms like a Security Information and Event Management (S I E M), but still struggle with noisy alerts and inconsistent investigations. That gap is rarely solved by adding yet another data source or buying another tool. It is solved by converting data into well-defined detection logic with clear intent, clear context, and clear next steps. When use cases are designed well, they raise detection fidelity and reduce analyst uncertainty, because the team understands what the alert means and what to do about it. When use cases are designed poorly, they create alert fatigue and a kind of learned helplessness, where analysts stop believing alerts represent real risk. The purpose here is to build use cases that are specific enough to trust, measurable enough to improve, and practical enough to operate under pressure.

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 use case is not just a rule that fires; it is a small operational contract between your telemetry, your detection logic, and your response workflow. It begins with a trigger, which is the observable condition you are using to detect behavior of interest, and that trigger must be stated in a way that can be tested against real data. It includes context, which is the surrounding information that makes the trigger meaningful, such as asset criticality, identity privilege level, typical baseline behavior, recent changes, and related events in adjacent systems. It also includes response, which is the expected investigation path and the decision points that follow, including what evidence must be gathered and what actions are appropriate at each confidence level. If you only define the trigger, you end up with alerts that demand interpretation every single time, and interpretation does not scale. If you define the trigger and context but not the response, you get beautiful detection that still produces inconsistent handling. A well-defined use case ties these pieces together so the alert is not a riddle but a guided starting point.

Starting with high-risk behaviors aligned to business priorities is how you avoid the trap of building use cases that look impressive but do not materially reduce risk. High-risk behaviors are not simply whatever is common in threat reports; they are the behaviors that, in your environment, create the most damage if missed. That might include privileged identity abuse, destructive changes to critical infrastructure, unauthorized access to sensitive data, or lateral movement in segments that contain crown-jewel systems. Business priorities matter because they tell you which assets and processes are truly critical, and that influences both detection scope and response urgency. A detection that fires on a low-impact system might be acceptable as informational, while the same behavior on a payment system or engineering pipeline should be treated as urgent. When you align use cases to priorities, you also earn stakeholder support, because your detection roadmap clearly maps to what leadership cares about protecting. This alignment helps analysts too, because it provides a rationale for why an alert matters, which improves decision confidence and reduces the feeling of chasing random signals.

Writing a use case with clear success criteria is a discipline that forces the team to move from vague intent to measurable outcomes. Success criteria should define what a good alert looks like, what evidence should be present when the use case fires correctly, and what level of investigation time is reasonable before a decision can be made. You want to capture what constitutes a true positive in your context, what constitutes a likely benign explanation that should be documented and closed, and what constitutes uncertainty that requires escalation. Success criteria should also include operational characteristics, such as acceptable alert volume per day, expected false positive rate, and what data sources are required for the use case to be reliable. This is where you protect yourself from building detections that are technically correct but operationally unusable. A use case that fires constantly is not successful, even if it occasionally catches something real, because it destroys attention and delays response to higher-quality signals. When success criteria are explicit, tuning becomes purposeful rather than emotional, and improvement becomes a normal engineering loop.

Tuning thresholds is where detection fidelity is either refined into something dependable or degraded into noise, and the best tuning is grounded in evidence rather than fear. Thresholds can include counts, timing windows, baselines, and risk scoring conditions, and they should be chosen based on how your environment behaves under normal conditions. If a behavior is common and benign, you should not alert on it broadly; you should add context that separates risky instances from routine ones. If a behavior is rare but high impact, you can accept a lower threshold, but you should compensate with stronger enrichment so analysts can validate quickly. Tuning should also consider attacker adaptation, because some thresholds can be evaded if they are simplistic, such as fixed counts in fixed windows without considering variability across systems and identities. The goal is not to eliminate all false positives, because that often means blinding yourself to real threats. The goal is to reduce noise enough that each alert earns attention, while preserving sensitivity to the behaviors that matter most.

Broad rules that alert constantly are one of the fastest ways to teach helplessness, and that helplessness spreads quietly through a S O C like a cultural infection. When analysts receive endless alerts that rarely lead to meaningful outcomes, they learn that attention is wasted and that closure is a survival skill. Over time, that turns into shallow investigations, delayed escalations, and a tendency to treat important alerts as just another ticket. Broad rules are often created with good intentions, such as wanting broad coverage for a tactic, but coverage without fidelity is not coverage, because the team cannot act on it consistently. This pitfall also wastes engineering time, because analysts spend their days documenting benign explanations instead of learning investigative patterns that matter. It can even harm relationships with stakeholders, because noisy alerts translate into noisy escalations, and stakeholders stop trusting the S O C. The fix is not more pressure on analysts; the fix is narrower, better-defined use cases that respect operational attention as a limited resource. When you prevent helplessness, you preserve the team’s ability to respond decisively when a real intrusion occurs.

Iterating use cases using analyst feedback and outcomes is one of the simplest ways to improve detection fidelity, and it also strengthens analyst confidence because it proves the system listens and adapts. Analysts are the closest observers of what works and what fails, because they live in the details of false positives, missing context, and confusing correlations. Feedback should be structured enough to be actionable, capturing what evidence was missing, what benign patterns are repeatedly triggering, and what response steps are unclear. Outcomes matter because the final disposition of cases, whether confirmed malicious, benign, or indeterminate, is the truth source for improving the detection logic. When you connect feedback to outcomes, you can identify whether the use case is firing for the right reasons and whether it is producing the intended response decisions. This also helps detection engineers avoid tuning in isolation, where changes are made based on assumptions rather than the lived reality of investigations. Over time, a tight loop between analyst experience and use case evolution raises both quality and morale, because the work becomes progressively more effective rather than endlessly repetitive.

An impossible alert volume is not a personal failure; it is a signal that your detection system is out of balance, and it demands smarter tuning rather than heroic endurance. In environments where the S I E M ingests massive telemetry, early detection efforts often produce a flood of alerts because the logic is too broad, the baselines are not established, or the enrichment is too weak to separate normal from abnormal. The right response is to triage at the use case level, identifying which use cases are generating the most noise relative to value, and then narrowing scope or adding context until the alert stream becomes manageable. This might involve restricting a use case to high-value assets, privileged identities, or confirmed administrative tools, rather than applying it universally. It might involve adding correlation requirements, such as requiring a suspicious process execution plus an anomalous authentication pattern within a defined window, rather than alerting on either alone. This kind of tuning is not about hiding alerts; it is about constructing detections that create meaningful investigative starting points. When alert volume becomes survivable, analysts regain the ability to think clearly and act confidently.

Enrichment data is one of the most powerful levers for improving triage speed and accuracy, because it turns raw events into a story that can be validated quickly. Enrichment can include asset inventory, business ownership, sensitivity classification, patch and vulnerability context, identity privilege level, recent change activity, and known-good operational patterns. It can also include correlation across platforms such as Endpoint Detection and Response (E D R) telemetry, identity provider audit logs, and cloud control plane activity, because threats rarely stay confined to a single data source. The most useful enrichment is the kind that answers the analyst’s first questions immediately, such as what is this system, who owns it, is this identity privileged, and is there a benign reason for this pattern right now. Without enrichment, analysts spend their time hunting for context rather than evaluating risk, and that slows containment decisions. Enrichment also reduces inconsistent decisions, because two analysts with the same enriched view are more likely to reach the same conclusion. As enrichment improves, the S O C shifts from manual context gathering to focused investigation, which is exactly what improves both fidelity and confidence.

Documenting expected actions is what keeps response consistent, especially as teams grow, rotate shifts, or rely on partners for parts of the workflow. Expected actions should describe what an analyst should do at different confidence levels and severities, including what evidence must be gathered, what stakeholders should be notified, and what containment options are appropriate given the environment. This documentation should also identify decision boundaries, such as when an analyst can close a case with evidence, when escalation is required, and when incident leadership must be engaged. Consistency matters because inconsistency is expensive, leading to repeated debates, duplicated effort, and uneven stakeholder experience. Documentation does not need to be rigid or overly long, but it must be clear enough to guide action under stress. It should also be updated as the use case is tuned, because response expectations change when the detection logic changes. When expected actions are aligned to the use case definition, alerts become operationally meaningful, and analysts develop confidence that they are making decisions the organization will support. That confidence directly improves response tempo during real incidents.

A good memory anchor is that strong use cases are specific, tested, and improved, and you can use that phrase as a mental filter whenever a new idea appears. Specific means the use case targets a defined behavior in a defined context, rather than trying to detect everything with a single broad rule. Tested means the logic has been validated against real telemetry and known scenarios, and its success criteria have been evaluated rather than assumed. Improved means the use case is treated as living content, refined using feedback, outcomes, and environmental change, rather than being created once and left to decay. This anchor helps you avoid both extremes, where you either obsess over perfect detection before deploying anything or you deploy noisy detection and hope it will somehow mature on its own. Real maturity comes from iterative engineering with operational feedback. The anchor also reinforces that detection content is a product with a lifecycle, not a pile of rules. When you apply this mental model consistently, you end up with fewer but better use cases, and the team trusts the alert stream because each alert has earned its place.

Retiring stale use cases is part of maintaining detection fidelity, and it is also a kindness to analysts because it reduces noise that no longer provides value. Use cases become stale when the environment changes, such as when a legacy system is decommissioned, a business process shifts, or a tool is replaced, leaving behind detections that fire on new normal behavior. They also become stale when attackers change techniques or when your telemetry improves in ways that render older logic redundant. A stale use case can also be one that theoretically detects something important but does so with such poor fidelity that it consumes attention without producing actionable outcomes. Retirement should be evidence-based, using outcomes, quality reviews, and stakeholder feedback to decide whether a use case still contributes to risk reduction. Retirement does not mean forgetting what you learned; it means capturing lessons and removing operational burden that no longer pays off. When you retire stale content deliberately, you keep the detection set lean, understandable, and maintainable. This maintenance discipline prevents the gradual slide into alert overload that makes teams reactive and exhausted.

As a mini-review, keep three traits of a strong use case close to mind, because they will guide your design instincts over time. A strong use case is precise, meaning it describes a specific behavior and the conditions under which that behavior is significant in your environment. It is evidence-driven, meaning it has clear success criteria and is validated against real data and real outcomes rather than assumptions or generic patterns. It is operable, meaning it includes sufficient context and expected actions so analysts can triage and respond consistently without reinventing the workflow each time. These traits work together, because precision without operability creates brilliant detections that no one can act on, and operability without precision creates structured noise that still overwhelms the team. Evidence-driven refinement is what keeps the other two traits true over time as the environment shifts. When you evaluate use cases with these traits, you naturally prioritize high-value detections and avoid content that looks sophisticated but fails in practice. The result is a detection program that steadily becomes more trusted and more effective.

To conclude, draft one new high-value use case today and treat it as a small, testable investment in both detection fidelity and analyst confidence. Choose a behavior that matters to your business priorities, define the trigger with enough specificity to be testable, and describe the context that separates risk from routine. Then state the expected response actions at a level that supports consistent handling, including what evidence should be gathered and what escalation boundaries apply. Define success criteria that include both security value and operational usability, because a use case is only successful if the team can act on it reliably. Once it is deployed, review outcomes and analyst feedback quickly, then tune with intention rather than waiting for frustration to build. Over time, this habit of drafting, testing, and improving use cases will do more to raise the maturity of your S O C than chasing ever-larger data volumes. You are building not just detections, but a system of trust between signals and decisions. Replace helpless noise with specific, tested content, and you will see both fidelity and confidence rise together.