Episode 58 — Drive detection engineering with intel requirements
In Episode 58, Drive detection engineering with intel requirements, we turn our attention to how intelligence should actively shape what your security tools look for every day. Detection engineering is where abstract understanding of adversary behavior becomes concrete logic that either catches activity or misses it entirely. This episode focuses on using intelligence not as background reading, but as a design input for better detection rules. When intelligence and detection engineering are disconnected, rules tend to drift toward generic coverage that produces noise instead of insight. When they are aligned, detection becomes sharper, more relevant, and more resilient to change. The goal here is to help you think like both an analyst and a builder, using evidence to decide what deserves to be detected and how.
Detection engineering is the process of building and tuning rules that are intended to find specific attacker behaviors in real environments. It is not simply about enabling alerts or copying templates from a vendor library. Effective detection engineering requires a clear idea of what behavior matters, why it matters, and how it appears in telemetry. This is where intelligence plays a decisive role, because it provides context about real adversaries rather than hypothetical ones. Without that context, detection rules often chase coverage for its own sake. When intelligence informs detection engineering, rules are designed to surface meaningful activity rather than just unusual activity. That distinction determines whether a security team spends its time responding or triaging.
One of the strongest inputs into detection logic is observed adversary tactics, techniques, and procedures (T T P s). These behaviors represent how attackers actually operate, not how tools assume they operate. By studying confirmed activity across incidents, you can identify patterns that recur even when surface indicators change. These patterns might include how attackers execute commands, how they move laterally, or how they establish persistence. Using these observations to define alert logic ensures that detections are grounded in reality. Rather than guessing what an attacker might do, you are encoding what they have already done and are likely to do again.
A common pitfall in detection design is focusing too heavily on static indicators such as I P addresses or domains. These indicators are easy to collect and easy to implement, but they are also easy for attackers to change. When detection relies primarily on static values, it becomes brittle and short-lived. Intelligence can help redirect focus toward behaviors that are harder to alter quickly. For example, an attacker may rotate infrastructure daily, but they may reuse the same execution flow or privilege escalation approach for months. Designing detections around those behaviors increases longevity and reduces maintenance churn. This shift from indicators to behavior is a hallmark of mature detection engineering.
Prioritization is another area where intelligence-driven requirements make a critical difference. Not every technique deserves equal attention, especially when resources are limited. Intelligence helps identify which techniques are most likely to be used against your organization based on industry, geography, technology stack, and past targeting. By prioritizing detection development for those techniques, you concentrate effort where it will have the greatest impact. This approach avoids the trap of spreading detection effort thinly across every possible threat. It also helps justify why certain detections are built first, which is important when teams must explain tradeoffs to leadership.
To see how this plays out in practice, imagine designing a rule that catches an attacker using a very specific and rare command sequence. That sequence may not appear in generic threat lists, but intelligence shows it is favored by a particular actor you track. Because the behavior is uncommon in legitimate activity, the detection can be both precise and low-noise. This kind of rule is difficult to design without deep understanding of adversary behavior. It also tends to be more valuable than broad anomaly detection, because it signals intent rather than coincidence. Intelligence provides the insight needed to identify these opportunities.
A useful way to think about intel-driven detection is as a custom-made lock designed for a very specific type of key. Generic locks stop casual attempts but are easy to pick by anyone determined. A lock designed for a specific threat makes the attacker’s job harder and increases the chance of detection. In detection engineering terms, this means building rules that are tailored to known behaviors rather than generic patterns. These rules do not need to catch everything, but they need to catch what matters most. Intelligence tells you which keys are most likely to be tried against your door.
Designing effective detection rules also requires identifying the specific logs and telemetry needed to support the logic. Intelligence can inform this requirement by clarifying which actions matter and where they are visible. If an adversary technique relies on a particular system call, process relationship, or network pattern, then detection depends on having that data available. This insight can drive logging decisions and visibility improvements. Without clear requirements, teams may collect large volumes of data without knowing what it is for. Intel-driven requirements give purpose to telemetry and help justify investment in logging and instrumentation.
This approach ensures that security tools are focused on the most relevant and dangerous threats rather than on abstract coverage metrics. Coverage is meaningful only when it aligns with real risk. Intelligence provides that alignment by connecting detection effort to adversary intent and capability. When tools are tuned based on intelligence, alerts become more actionable and less frequent. This improves defender confidence and reduces alert fatigue. Over time, it also improves trust between intelligence and operations, because detections clearly reflect shared priorities.
Testing is a critical step in turning intelligence requirements into effective detections. Running new rules against historical data helps validate whether the logic would have caught past attacks. This testing provides feedback on both sensitivity and specificity. If a rule never fires on known malicious activity, it may be too narrow. If it fires constantly on benign activity, it may be too broad. Intelligence helps interpret these results by providing context about what should have been seen. Testing turns theory into evidence and helps refine detection logic before it reaches production.
Detection engineering does not end once a rule is deployed, because adversary behavior continues to evolve. As tracked actors adjust their T T P s, detection logic must adapt as well. Intelligence is the signal that tells you when a rule may be losing relevance or when a new one is needed. Regular review ensures that detection stays aligned with current threat behavior rather than historical patterns. This ongoing adjustment prevents stagnation and keeps defenses responsive. It also reinforces the idea that detection engineering is a living process, not a one-time project.
False positives are one of the fastest ways to undermine confidence in detection. Verifying that new rules do not generate overwhelming noise is therefore essential. Intelligence can help here by clarifying what legitimate activity looks like versus what is truly suspicious. Understanding context reduces the temptation to over-alert on rare but benign behavior. When false positives are kept low, defenders are more likely to trust and act on alerts quickly. This trust is critical for timely response and effective defense.
Practice is what turns these concepts into skill. Mapping a single adversary technique to a specific detection query forces you to think through the entire chain from behavior to telemetry to logic. The exercise reveals gaps in visibility and assumptions in reasoning. It also highlights how intelligence must be translated into precise conditions rather than vague descriptions. Practicing this regularly improves both analytic clarity and engineering discipline. Over time, the translation from intelligence to detection becomes faster and more reliable.
Driving detection engineering with intelligence requirements changes the role of intelligence from observer to enabler. It ensures that analysis directly influences what systems watch for and how they respond. When intelligence drives detection, defenses become proactive rather than reactive. Write one detection requirement based on a known actor T T P, because that requirement is the point where understanding becomes protection.
