Episode 14 — Mine internal telemetry for durable intelligence wins
In Episode 14, Mine internal telemetry for durable intelligence wins, we focus on a truth that gets lost when teams chase the newest feed or the loudest headline. The most durable intelligence advantage you have is the data your own environment produces every day. External reporting can help you anticipate what is possible, but internal telemetry tells you what is actually happening to you, in your networks, with your users, and across your systems. This episode is about learning to treat internal data as a primary intelligence source rather than as a pile of logs that only matters after an incident. When you mine internal telemetry consistently, you find unique threats earlier, you validate assumptions faster, and you build patterns that are resilient to changes in attacker tooling. The goal is not to collect more data, but to extract lasting value from what you already have.
Internal telemetry includes signals from endpoint detection platforms, identity systems, authentication logs, and network monitoring sources that observe traffic inside your boundaries. Endpoint visibility often captures process execution, command-line activity, file operations, and persistence behavior on individual systems. Network monitoring captures connections between systems, service-to-service communication, and unusual flows that indicate lateral movement or staging. Together, these sources form a map of behavior that is specific to your environment. That specificity is what makes the data so valuable, because it reflects your architecture and your business processes. External sources can tell you that a technique exists, but internal telemetry can tell you whether that technique is appearing in your ecosystem. When you treat this telemetry as intelligence input, you stop relying on generic threat narratives and start working from evidence you can directly verify.
A practical place to start is with administrative accounts, because successful logins in privileged contexts are high-value events. Analyzing patterns of successful logins for administrative accounts helps you detect unusual access that may not trigger a traditional alert. Look for changes in timing, origin, device posture, and frequency that diverge from known patterns. A privileged login at an unusual hour, from an unfamiliar host, or from a new geographic pattern can be a signal even when the credentials were valid. Attackers who obtain privileged access often try to blend in by using legitimate accounts, and your detection advantage comes from knowing what normal looks like for those accounts. When you review successful access patterns rather than only failures, you shift from reactive to proactive. That shift is one of the most reliable ways to catch early compromise in targeted campaigns.
Many teams unintentionally neglect internal logs because they feel messy and because external intelligence feels easier to consume. Avoiding this mistake is critical, because internal logs often contain the most relevant clues when an attack is targeted and tailored. Targeted attackers do not behave like commodity malware at scale, and their traces can be subtle. Internal telemetry is where those subtle traces accumulate into patterns over time. When you ignore internal logs, you risk missing early indicators that are specific to your environment, such as unusual access to a niche system or a service account being used in an unexpected workflow. Internal logs also allow you to validate external claims quickly, which prevents wasted effort. The more you rely on internal evidence, the more confident your conclusions become.
File shares are another area where baseline-driven detection can deliver strong results, especially when attackers stage data before exfiltration. Establishing a baseline for normal file share access allows large-scale staging to stand out, even when the individual actions look legitimate. The baseline should capture which users or service accounts access which shares, how much data movement is typical, and what time-of-day patterns are normal. Data staging often shows up as a shift in volume, a new pattern of file enumeration, or unusual access from hosts that do not normally interact with certain shares. These patterns are not always dramatic, but they are often measurable. The baseline gives you context, and context is what turns activity into signal. When you can say this access pattern is abnormal for this environment, your confidence rises quickly.
Now picture a moment that often becomes the pivot point in an investigation. You identify an internal host communicating with a server it has never contacted before, and the connection repeats with a steady rhythm. This is a classic internal anomaly that can signal lateral movement, new service discovery, or command and control behavior that has shifted into internal infrastructure. The key is that the novelty itself is the signal, because most internal communication patterns are stable over time. A new connection might be benign, such as a software update or a new service deployment, but it still warrants understanding. You look at the host role, the timing, the ports, and whether the communication aligns with known operational changes. By treating novelty as a prompt for validation, you discover issues earlier without overreacting. Over time, this habit builds a strong internal detection posture that is difficult for attackers to avoid.
Internal data is your home-field advantage because you know what normal behavior looks like, and attackers do not. Adversaries operate with incomplete knowledge of your environment, and they are forced to probe, guess, and adapt. Your advantage is that you can recognize when behavior diverges from established patterns. This is why baselining is not a luxury, it is a practical weapon. When you have a baseline, anomalies become obvious and investigations become faster because you are not starting from zero. You are comparing current behavior to expected behavior. This approach also scales because you can apply it across identities, endpoints, and network communications. Home-field advantage becomes even stronger when it is reinforced with consistent review and feedback loops.
It is also important to be able to explain internal telemetry differences clearly, especially when you are guiding junior analysts. Endpoint telemetry tends to be rich in execution context, showing what ran, who ran it, how it was launched, and what files or registry locations were touched. Network-based telemetry tends to be rich in connectivity context, showing which systems talked, how often, and how much data moved, but not always what happened inside the session. Endpoint data can answer questions about process lineage and persistence, while network data can answer questions about movement and external communication patterns. Both are incomplete on their own, which is why correlation matters. When you can summarize these differences plainly, you help your team choose the right data source for the question at hand. That clarity reduces wasted effort and improves investigative speed.
Internal telemetry is also valuable because it allows you to measure whether your security controls are actually working against observed techniques. It is easy to assume that a control is effective because it is deployed, but effectiveness is proven by observing whether it detects, blocks, or limits real activity. If you observe repeated suspicious behavior that your control should have detected, that is a signal for tuning or improvement. If you observe that certain techniques are consistently blocked, that is evidence you can share with leadership to justify continued investment. Internal data becomes a feedback mechanism for your security program, not just a record of events. This turns telemetry into a strategic asset, because it supports decisions about budgets, tooling, and priorities based on reality. When you measure effectiveness, you stop arguing from theory.
A particularly important set of signals involves living-off-the-land behavior, where attackers use built-in tools rather than obvious malware. Telemetry showing the execution of PowerShell (P o w e r S h e l l) or Windows Management Instrumentation (W M I) scripts often reveals attacker activity that blends into administrative noise. The defensive challenge is that legitimate administrators also use these tools, which is why context is essential. Look for unusual command-line patterns, execution from unexpected parent processes, suspicious script locations, and timing that does not align with normal admin workflows. The objective is not to label every use as malicious, but to identify execution that deviates from baseline. Living-off-the-land activity often becomes the bridge between access and impact, so it deserves focused attention. When you can detect abnormal tool usage early, you reduce the chance of deeper compromise.
Correlation is where internal telemetry becomes truly decisive, especially when you connect process execution to network behavior. If you see a suspicious process execution and then observe a new outbound connection from the same host to an unusual destination, the combination is stronger than either signal alone. Correlating internal process logs with external network connections can reveal active command and control channels, even when encryption hides content. The pattern of execution leading to communication is often more telling than any single indicator. This correlation also helps you move from suspicion to a working hypothesis quickly, which accelerates response. By linking what ran to where it connected, you build a story of intent and action. That story is what makes intelligence defensible and useful.
To support this kind of analysis, your internal logging must capture enough detail to reconstruct the full path of suspicious file execution. Without execution context, you may know that something happened but not how it happened, which limits response and remediation. You need to know where the file came from, what launched it, which user context it ran under, and what it touched afterward. That reconstruction is how you determine whether the activity was accidental, legitimate, or malicious. It is also how you identify the initial vector and prevent recurrence. Logging detail is not about hoarding data, it is about capturing the fields that allow you to answer the questions that matter. When your logs are rich enough, your investigations become faster and more accurate.
Service accounts are another high-value focus area because they often have broad access and run continuously. Regularly reviewing your most active internal service accounts for signs of misuse helps you detect credential theft that would otherwise blend into normal operations. Look for unusual authentication paths, unexpected interactive logins, new host associations, or changes in access patterns that do not match established workflows. Service accounts are attractive to attackers because they can provide persistence and lateral movement opportunities without requiring user interaction. They also often bypass certain controls due to operational necessity. That reality makes monitoring essential, not optional. When you watch service accounts with baseline awareness, you protect one of the most commonly abused pathways in enterprise environments.
Your internal data is a goldmine because it reflects the truth of your environment, not just generalized threat narratives. When you mine it consistently, you build durable detection advantages that remain effective even as attacker tools and indicators change. The next step is to pick one internal log source and audit it for anomalies, using baseline thinking rather than hunting for dramatic events. The goal is to reinforce the habit of observation and to strengthen your mental model of normal behavior. Over time, this habit turns into early detection, faster investigations, and better intelligence products for every audience. Internal telemetry is not just a record of the past, it is your best lever for shaping what happens next.
