In this episode, we’re going to talk about something that sounds like it should be simple but often isn’t: why security sensors sometimes miss real attacks, and why they sometimes scream about harmless activity. Intrusion Detection System (I D S) and Intrusion Prevention System (I P S) tools are supposed to help you notice malicious behavior and, in some cases, block it, but real networks are messy and the tools are only as good as how they are used. Observability is the broader idea of being able to see what is happening in your environment well enough to understand it, troubleshoot it, and defend it. When observability has gaps, defenders are forced to guess, and guessing is a great way to lose time or miss important clues. The good news is that many failures in I D S, I P S, and observability come from a few predictable causes: rule quality problems, bad placement, false positives that bury the real signals, and coverage gaps where traffic or events simply aren’t being observed. Our goal is to build a clear beginner-friendly way to diagnose those causes, so you can explain what’s wrong and what needs to change.
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A useful first step is to understand the difference between detection and prevention, because that difference changes how you judge success. An I D S watches traffic or activity and produces alerts when it thinks something suspicious is happening, but it does not stop the traffic by itself. An I P S sits in a position where it can actively block or modify traffic based on rules, which means it can stop certain attacks in real time but also has a higher risk of disrupting legitimate activity. Observability is not a single tool, but the combination of sensors, logs, telemetry, and context that helps you turn raw events into understanding. If you have good observability, you can confirm whether an alert represents a real problem, and you can also confirm when no alert happened but something still went wrong. Beginners sometimes assume that if an I D S does not alert, nothing bad happened, but that is not safe thinking. A silent sensor can mean everything is fine, or it can mean the sensor did not see the traffic, did not have the right rules, or did not understand what it saw. So the first mindset shift is to treat sensors as fallible observers, not as perfect judges.
Rule quality is often the most immediate cause of noisy or blind detection, and it starts with what a rule is actually trying to match. Many I D S and I P S rules look for patterns that are associated with attacks, such as suspicious protocol behaviors, known exploit signatures, or command-and-control indicators. If the rule is too broad, it matches normal behavior and produces lots of false positives. If the rule is too narrow, it misses variations of the attack and produces false negatives, meaning the attack happens without an alert. Quality also includes context, like whether the rule is relevant to your environment’s actual technologies. A rule for a service you do not run may create noise without adding value, and a missing rule for a service you do run can create a dangerous blind spot. Beginners should also understand that rule sets age, because attacks evolve and environments change, so a rule that was useful last year might be noisy or ineffective now. Improving rule quality is therefore a process of alignment: the rules should reflect current threats and your actual assets.
A practical way to think about rule quality is to imagine a smoke detector that is too sensitive or not sensitive enough. If it goes off every time you make toast, people will eventually ignore it or disable it, which is worse than having no detector at all because it creates false confidence. If it is not sensitive enough, it may not trigger until the house is already on fire. Security rules are similar because they must find a balance that produces actionable signals without overwhelming defenders. That balance depends on the environment’s normal behavior, which is why baseline knowledge matters. If you do not know what normal traffic looks like, you cannot tune rules effectively, and you will either accept noise or miss threats. Rule quality also depends on clarity of what the alert means, because an alert that says suspicious traffic without any detail is hard to act on. So part of fixing rule quality is ensuring rules produce alerts with enough context to support a next step, such as identifying the source, destination, and suspected technique.
Placement is another common cause of I D S and I P S failure, and it is surprisingly easy to get wrong because modern networks have many paths. If a sensor is placed where it does not see important traffic, it cannot detect attacks in that traffic, no matter how good the rules are. In older networks, placing a sensor at a central gateway might capture much of the traffic, but in modern environments, traffic may flow east-west between internal systems, between cloud workloads, or between remote users and cloud services without passing through a single chokepoint. Placement also matters for performance, because a sensor placed in a high-throughput path might drop packets or miss details if it cannot keep up. For an I P S, placement is even more delicate because it sits inline, meaning it can become a single point of failure if it is overloaded or misconfigured. Beginners should learn to ask, what traffic does this sensor actually see, and what traffic does it miss. That question alone often explains why attacks were not detected, or why alerts seem inconsistent.
Another placement issue that shows up often is encryption, because more and more traffic is protected by T L S, and sensors cannot inspect what they cannot see. If a sensor is watching encrypted traffic but has no way to access the decrypted content, its detection may be limited to metadata such as destination, timing, and certificate information. Sometimes that is enough, but often it is not, especially for detecting payload-based signatures. This can create a gap where defenders assume the I D S is inspecting traffic deeply, but in reality it is only seeing outer wrappers. A beginner-friendly way to understand this is to think of listening to a conversation in a language you do not understand; you can still notice who is talking and how often, but you cannot understand the actual words. Fixing this gap can involve shifting detection approaches toward behavior and metadata, placing sensors closer to endpoints where data is available, or using other telemetry sources that capture relevant events. The key is honesty about what visibility exists, because pretending you can inspect encrypted content when you cannot leads to blind spots.
False positives are a major operational problem because they consume attention, and attention is one of the most limited resources in security. When an I D S generates too many false positives, the team spends time triaging harmless events, and real attacks can hide in the noise. False positives also damage trust in the detection system, because people start assuming alerts are wrong. A beginner should understand that false positives can come from rules that are too broad, but they can also come from normal business activity that looks suspicious without context. For example, legitimate vulnerability scanning, software updates, or backups can produce patterns similar to malicious scanning or data transfer. Fixing false positives is not just about turning off rules; it is about understanding why the rule triggers and then adding context to differentiate benign from malicious. That might include refining thresholds, adding exceptions for known safe sources, or adding additional conditions that must be met before an alert is raised. The goal is to keep sensitivity where it matters while removing the constant distractions.
One important misconception is that reducing false positives always makes detection weaker, but that is not necessarily true when done thoughtfully. If you remove noise, defenders can respond faster to what remains, and the overall security outcome improves. The trick is to reduce false positives without creating new false negatives, which is why tuning should be based on evidence and careful observation. A safe approach is to examine a sample of alerts, classify why they are happening, and identify patterns that represent normal behavior. Then you adjust rules to account for those patterns while still catching abnormal versions of the behavior. For example, if a rule triggers on file transfer activity, you might tune it to focus on unusual destinations, unusual times, or unusual volumes rather than every transfer. Beginners should also learn that some false positives are unavoidable, and the goal is not zero, but manageable. A detection system that produces a small number of high-quality alerts can outperform a system that produces thousands of low-confidence alerts.
Coverage is the bigger concept that ties everything together, because coverage is about whether your detection strategy actually spans your assets, networks, and workloads. You can have excellent rules and a well-placed sensor, but if some parts of the environment have no sensors, no logs, or no telemetry, those areas become hiding places. Coverage gaps happen when new systems are deployed without integrating logging, when cloud environments are treated as separate islands, or when remote access changes how traffic flows. They also happen when endpoints are not running the necessary telemetry agents or when servers are treated as too sensitive to monitor. A beginner-friendly way to see coverage is to imagine trying to secure a building with cameras but leaving some hallways without cameras. An attacker will choose the hallway without cameras, not because it is clever, but because it is unobserved. Fixing coverage means identifying what is unobserved and deciding what signals are needed to make it observable. Sometimes that means adding network sensors, but often it means collecting endpoint telemetry, server logs, and identity signals that provide a more complete picture.
Observability gaps also occur when data exists but is not usable, which is a different kind of coverage problem. If logs are collected but not time-synchronized, it becomes hard to build a timeline. If logs are stored but not searchable, they might as well not exist during an incident. If data sources use inconsistent formats or lack key fields, correlation becomes weak. Beginners should realize that observability is not just about collecting everything, because collecting everything without organization creates its own form of blindness. Good observability means the right data is collected, the data is reliable, and the data can be connected across sources to tell a coherent story. That story might include network flows, endpoint events, authentication events, and application logs that align in time and identity. When observability is strong, you can validate I D S or I P S alerts quickly and also detect attacks that do not match known signatures. So improving observability supports both detection and investigation.
A practical troubleshooting mindset for I D S, I P S, and observability problems is to start with what you expected to detect and then ask what evidence would prove the system was capable of detecting it. If an attack happened and there was no alert, you ask whether the relevant traffic passed the sensor, whether the sensor could inspect it, and whether a rule existed that would match it. If there was an alert but it was wrong, you ask what the rule matched, what normal behavior caused it, and what additional context could reduce the noise. If defenders feel blind, you ask what assets lack telemetry, what logs are missing, and where traffic flows outside observed paths. This mindset keeps you from defaulting to blame, such as blaming the tool or blaming the user, and instead focuses on verifiable capability. It also encourages incremental improvements, because you can fix one gap at a time and measure the effect. Over time, this becomes a disciplined practice: define what you want to detect, ensure visibility exists, tune rules for quality, and confirm coverage across the environment.
It is also important to discuss the tradeoffs between I D S and I P S, because placing a prevention system inline introduces both security benefits and operational risks. An I P S can block attacks automatically, which is valuable for high-confidence threats, but if rules are wrong or traffic patterns change, it can block legitimate activity and cause outages. This risk sometimes leads organizations to run I P S in detection-only mode, which reduces operational risk but also removes automatic blocking. Beginners should not see this as a failure, but as an engineering choice based on risk tolerance and confidence in rules. The safer route for many environments is to begin with detection, improve rule quality, reduce false positives, and only then enable prevention for a narrow set of high-confidence cases. That progression matches how trust in the system is built. If you start with aggressive prevention before you have observability and tuning, you can create a situation where security controls are blamed for outages and then disabled, leaving everyone worse off.
To conclude, fixing I D S, I P S, and observability gaps comes down to making detection meaningful, placed correctly, and supported by broad, reliable visibility. Rule quality determines whether alerts are actionable or noisy, placement determines whether the sensor even sees what matters, false positive control determines whether humans can respond effectively, and coverage determines whether attackers have unobserved spaces to exploit. Observability is the glue that helps you validate signals and build confidence in decisions, because it turns isolated alerts into understandable stories. When you approach these problems with a calm chain-of-evidence mindset, you stop treating security monitoring as mysterious and start treating it as an engineering system that can be improved. The biggest beginner takeaway is that detection is never automatic just because a tool is present; capability depends on rules, visibility, and context. When those pieces align, your monitoring becomes sharper, your response becomes faster, and attackers have fewer places to hide.