Certified: The GIAC GSLC Audio Course

In this episode, we settle into a practical truth that experienced defenders learn the hard way: endpoint monitoring only works when you treat coverage and tuning as intentional engineering, not as a checkbox you hope will behave itself. You can buy excellent tools and still miss what matters if endpoints are not consistently visible, if signals are chosen carelessly, or if noise drowns out the few behaviors that actually indicate risk. The good news is that the path to strong endpoint monitoring is not mysterious, and it does not require magical detection logic. It requires disciplined thinking about what you can observe, where you can observe it, and how you turn observation into decisions that reduce uncertainty. We will move through that path in sequence, because the sequence matters, and reversing it usually creates frustration, distrust in alerts, and gaps you only discover after damage is done.

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 place to start is by defining telemetry in a way that keeps you honest. Telemetry is the stream of events that describe behavior over time, not merely the subset of those events that your tooling turns into alerts. Alerts are opinions, and opinions can be wrong, incomplete, or biased by the assumptions that shaped the detection rule. Telemetry is closer to raw evidence, and evidence is what you need when you are trying to understand what happened and what is happening right now. When an analyst says they have no visibility, what they often mean is that the alert did not fire, and they do not have the underlying events to prove whether the behavior occurred. That distinction is crucial, because mature monitoring programs treat alerts as an output of analysis, while treating telemetry as the input they continuously refine.

Once you are thinking in terms of evidence, you can choose key signals with a clearer purpose. You are looking for signals that describe important actions on an endpoint, especially actions that attackers and malware cannot avoid when they move from intent to impact. Process starts are high value because they represent execution, and execution is where theory becomes reality. Network calls are high value because they show where an endpoint reaches out, what it talks to, and often when it begins behaving differently than its peers. Changes are high value because adversaries must alter something to persist, to disable defenses, to gain privilege, or to exfiltrate data. The art is not collecting everything, because everything is expensive and overwhelming, but collecting enough of the right things to support rapid confirmation and reliable investigation.

Process telemetry is especially foundational because it gives you the narrative of what actually ran. You want to know when a process started, who started it, what the parent process was, what the command line looked like, and what the process attempted to do next. This is where you see the difference between normal administrative activity and suspicious execution chains that only make sense if you have seen them before. A benign installer launched by a trusted software distribution mechanism has a different shape than a scripting engine launched by an email client spawning a child process with a strange command line. It is not about memorizing every pattern, but about ensuring you have the process story available when you need it. Without that story, you end up guessing, and guessing is expensive and often wrong.

Network telemetry complements process telemetry by showing what execution reached for outside the host. When a process makes outbound connections, you get clues about command and control, download activity, lateral movement attempts, and data movement. The key is to capture enough context to connect the dots, including destination, port, protocol, and timing relative to the process chain. Network signals often become meaningful only when you correlate them with what ran and who ran it. A connection to a cloud storage provider might be routine for one application and suspicious for another. That is why you do not want network telemetry floating on its own as a pile of destinations and ports with no link back to the endpoint behavior that initiated the traffic.

Change telemetry is the third pillar because compromise often involves modifying state. The state might be a registry key, a service configuration, a scheduled task, a browser extension, a credential store, a security setting, or a startup mechanism that ensures persistence. Changes also include attempts to tamper with logging, disable protective components, or alter policies that reduce detection. A subtle but important point is that defenders often focus on the moment of malware execution and forget that lasting harm requires durable changes. If you can see those changes and tie them back to the initiating process and user context, you can often separate a false alarm from a true incident quickly. Without change telemetry, persistence becomes invisible, and invisible persistence is how short incidents become long ones.

With signals chosen, coverage becomes the next hard requirement, because you cannot monitor what you cannot see. Coverage means your laptops, servers, and remote devices all generate telemetry consistently, in the same way, and with known exceptions that are documented and managed. This is where endpoint monitoring moves from a security conversation to an operational discipline. Laptops roam across networks, live on home routers, and disappear for weeks if an employee travels or uses a device offline. Servers may exist in data centers, cloud environments, and managed hosting platforms that have different constraints. Remote devices might include contractors, jump systems, or specialized assets that are easy to forget because they are not in the standard build pipeline. Effective monitoring treats those differences as design inputs and makes sure the telemetry still arrives, even when the environment is messy.

The most common pitfall is blind spots created by unmanaged devices or missing agents, and it usually shows up as an uncomfortable surprise. Someone assumes that an endpoint fleet is fully instrumented because the tool reports a high number of active endpoints, but the missing population is simply not represented. Another team spins up servers outside the normal deployment process, and those systems never receive the monitoring components. Remote workers use older machines that are technically on the corporate directory but are not receiving updates and do not report telemetry reliably. These blind spots are not theoretical; adversaries actively seek the quiet corners where defenders are least likely to notice. If your monitoring coverage is uneven, you are unintentionally marking certain areas as low-risk simply because you cannot see them.

A quick win that prevents many of these problems is maintaining an inventory and reconciling it on a routine schedule. Inventory here is not a static spreadsheet; it is a living view of what endpoints should exist, what they are, and whether they are producing telemetry. Reconciliation means comparing your authoritative endpoint list to what your monitoring platform reports as active, inactive, or absent. When done weekly, reconciliation helps you catch drift early, when it is still a simple operational correction rather than an incident response emergency. It also forces you to define what authoritative means, because organizations often have multiple partial inventories that disagree. The value of reconciliation is not just the list; it is the habit of proving coverage rather than assuming it.

Now consider what happens when an attacker disables the monitoring component, because this is an important scenario rehearsal for realistic operations. If an adversary has enough access to tamper with an agent, they are demonstrating intent and capability, and that alone should elevate concern. The real question is whether your monitoring reveals the gap fast enough to matter. If your only visibility is the agent itself, then agent disablement can look like silence, and silence can be misinterpreted as calm. Better approaches treat loss of telemetry as a signal, not as an absence of signals. You should know when an endpoint stops reporting unexpectedly, how quickly you can detect that condition, and what you do about it when it occurs. The goal is not perfect prevention, but rapid discovery so adversaries cannot hide behind missing data.

Tuning comes next, but only after coverage, because tuning without coverage creates a false sense of sophistication. Tuning is the process of turning raw telemetry and generic detection content into alerts that match your environment, your risk, and your operational capacity. A core technique is building baselines, which means understanding what normal looks like for your endpoints across time, roles, and business cycles. Normal for a developer workstation differs from normal for a point-of-sale system, and normal during patch week differs from normal on a quiet weekend. Baselines allow you to interpret deviations with less guesswork, because you can say not only that an action occurred, but that it is unusual in context. The point is not to ignore rare events automatically, but to treat rarity as one dimension of suspicion that must be weighed with other evidence.

Known-good behavior patterns are equally important, because defenders waste enormous time investigating what is simply routine but unfamiliar. If your environment uses specific management tools, automation frameworks, remote administration platforms, or deployment agents, those tools will produce behaviors that look like attacker tradecraft when viewed out of context. Tuning means teaching your monitoring program what those patterns look like so alerts can focus on truly unexpected behavior. That might involve allowing certain signed binaries, recognized command-line templates, or common parent-child process chains that are consistent with approved tooling. The key is to do this carefully and transparently, because tuning that is too aggressive becomes a mechanism for hiding problems rather than reducing noise. When tuning is done well, it does not remove visibility; it reduces distraction while preserving investigative power.

Correlation is where endpoint monitoring becomes truly effective, because endpoints do not operate in isolation. When you correlate endpoint signals with identity and network activity, you create a fuller picture of intent, access, and movement. Identity context might include which user was logged in, which credentials were used, and whether the authentication was typical for that user. Network context might include whether the endpoint’s connections align with expected destinations, whether lateral movement occurred, and whether the same destination is being contacted by multiple endpoints in a suspicious pattern. Correlation helps you answer questions that matter operationally, such as whether a process executed under an interactive user context or under a service account that should never be used that way. It also helps you distinguish a single odd event from coordinated activity that indicates compromise.

Suppression is a tool that should be handled with care, because it can either preserve sanity or bury truth. Suppression is meant to reduce repetitive alerts that do not add value, especially when the behavior is understood and accepted. The danger is that suppression can hide real attacks if it is applied broadly, permanently, or without clear boundaries. A safe way to think about suppression is to treat it as a narrow exception with conditions, not as a blanket dismissal. You might suppress a known noisy alert for a specific endpoint group and only when the behavior matches a well-defined pattern. You might also add time bounds and periodic review so suppression decisions do not become permanent simply because everyone forgot why they were added. The key operational principle is that you want less noise, not less truth, and any suppression that reduces truth should make you uneasy.

A useful memory anchor for the whole approach is coverage first, then tuning, then meaningful response, because this sequence prevents common failure modes. If you do tuning first, you optimize alerts for the endpoints you happen to see, while missing endpoints remain invisible and unaccounted for. If you respond before you tune, you burn out your analysts with noisy alerts that teach them to ignore the platform. When you start with coverage, you build confidence that what you are not seeing is less likely to be a monitoring failure. When you then tune, you shape the alert stream into something humans can act on without drowning. When you finally focus on response, you ensure the monitoring program reduces risk in practice, not just in dashboards and reports.

Escalation rules are how you turn monitoring into action when high-confidence suspicious behaviors occur. Escalation does not mean panic; it means a clear, consistent decision pathway that triggers the right level of attention. High-confidence suspicious behavior might include evidence of credential dumping, attempts to disable security controls, unexpected privileged process creation, persistence mechanisms appearing on endpoints that should be tightly controlled, or a process chain strongly associated with common intrusion patterns. The rule must define who gets notified, how quickly, and what initial steps are taken to preserve evidence and reduce harm. Escalation rules also protect teams from inconsistent decisions, because ambiguity causes delays and delays are where incidents expand. When escalation is well-designed, responders can move calmly and methodically, even under pressure, because the first moves are already agreed upon.

A practical way to check your understanding is to name multiple telemetry sources and explain why each matters, because this is how you test whether your visibility is diverse enough to support investigation. Process telemetry matters because it shows execution and causality through parent-child relationships and command-line context. Network telemetry matters because it shows communication patterns, lateral movement attempts, and external reach that often indicates command and control or data movement. Change telemetry matters because it exposes persistence mechanisms, configuration tampering, and defense evasion that attackers rely on for staying power. Identity telemetry matters because it links behavior to users and service accounts, clarifying whether actions align with legitimate roles or indicate misuse. When you can articulate why each source exists, you are less likely to over-rely on a single data stream that can fail or be manipulated.

As we close, the most useful conclusion is not abstract agreement that endpoint monitoring is important, but a concrete commitment to closing one monitoring gap you already suspect exists. That gap might be a set of remote laptops that do not report reliably, a server fleet built outside the standard pipeline, or a blind spot created by a missing integration that prevents correlation with identity and network data. The point is to pick one gap that is real and actionable, because closing gaps is how monitoring programs improve over time. When you do that, you reinforce the mindset that monitoring is a living system, not a product you install once. If you keep the sequence intact and stay disciplined about evidence, coverage, tuning, and response, endpoint monitoring becomes a steady source of clarity rather than a constant source of noise. And that is what makes it effective in the environments you actually have, not the perfect ones we all wish we had.