Key Takeaways
Network blind spots are often caused by lateral movement, encrypted channels, low-and-slow exfiltration, and hybrid telemetry gaps.
Network behavior monitoring (NBM) reveals these blind spots by profiling baselines, correlating context, and surfacing anomalies without decrypting everything.
Effective NBM requires full protocol coverage, contextual ML, actionable workflows, and stack integration to avoid alert fatigue.
Fidelis Security’s NDR and XDR capabilities — deep session inspection, behavioral ML, deception correlation, and unified response — are purpose-built to close these visibility gaps.
You are counting on lots of security measures to keep your network safe. The truth is that these measures can still have secret passages that bad people can use to sneak around without being noticed. You can have things like firewalls and special software, on your computers to watch for problems and still not catch people moving around inside your network taking data slowly or doing weird things that are not supposed to happen because these things do not always look like the problems you are expecting.
This is a problem because bad people know about it and they use it to their advantage: they can hide in your network for a long time collect important files and take your data without setting off any alarms, which is what you would expect to happen when someone is doing something wrong.
This is not a problem with the tools we use it is a problem with being able to see what is going on. When you cannot see what your network is doing and how it is working you cannot figure out what problems to fix how to fix them.
The answer to this problem is network behavior monitoring.
Network behavior monitoring is a way to always watch your network and see what is happening. It looks at all the traffic on your network. Helps you find things that are not normal, like why someone is doing something and what they are trying to do with network behavior monitoring. NBM does not replace the controls you already have in place. Instead it helps them by showing you what is really going on with your protocols, ports, clouds and on-prem segments.This means you can find problems faster and respond to them with confidence when you use NBM. NBM is really good at helping you see what is happening with your protocols, ports, clouds and, on-prem segments.
What are the common blind spots in traditional network security?
Silent lateral movement inside trusted zones
Attackers often go from one place to another inside the network. They do this when the security at the doors of the network is good. The security inside the network is not so good. This is because the rules that control what happens inside the network are not very strict.
When attackers move around like this they usually use the methods and accounts that everyone else uses. That is why the tools that look for signs cannot always catch them.
You need to look at what the devices, on the network’re doing. You need to see if a device is suddenly talking to a lot of hosts that it does not usually talk to. You also need to see if it is using services that it does not usually use. This is the kind of information that can help you stop the attackers.
Slow, low-and-slow data exfiltration
When attackers exfiltrate data in tiny pieces or embed it in normal-looking traffic, volumetric alarms don’t trip. Detecting small, repeated transfers or odd timing patterns requires baseline understanding of normal data flows and context (what hosts should move what data and to which destinations).
Encrypted traffic and hidden channels
There is a lot of encrypted traffic these days and organizations do not have the ability to decrypt all of it. This is because of privacy and performance issues. When organizations can only look at some of the traffic they have spots.
They use something called analysis to find suspicious activity. This analysis looks at things like the metadata of a session the timing and the patterns of data transfer. It can find things that do not seem right without having to decrypt all the data in the traffic.
This way organizations can still find encrypted traffic that seems suspicious, like encrypted traffic that is doing something. Organizations use analysis to look at encrypted traffic and find things that seem suspicious.
Complex hybrid and cloud networks
When you have architectures and you are using multiple cloud deployments it can create really complicated network systems. These systems have ways of collecting information and they can have blind spots. The problem is that the monitoring tools you use on your premises do not work well with cloud hosts or container traffic. This creates gaps in what you can see. You do not have a picture because the information you are getting is not the same, across all environments. Hybrid architectures and multi-cloud deployments are the issues here they make it hard to get a clear view of what is going on.
Network behavior monitoring is really good at showing us what is going on in our network. Other tools do not always do a job of this. Network behavior monitoring adds visibility to our network where other tools are not able to. This means we can see things that other tools miss. Network behavior monitoring helps us understand what is happening in our network. It does a better job than other tools in many cases. Network behavior monitoring is very important, for keeping our network safe and running smoothly.
Digital Investigations
The Challenge
Core Forensic Capabilities
Deep Session Inspection® for Forensics
How does network behavior monitoring (NBM) add visibility where other tools fall short?
Baseline profiling and anomaly detection
NBM builds statistical baselines for hosts, services, and flows so it can spot deviations that matter — new peer relationships, unusual port usage, or changes in data transfer patterns — even when those activities use legitimate protocols or encrypted channels.
Contextual correlation across telemetry sources
Network Behavior Monitoring correlates flow metadata, Domain Name System, authentication logs and endpoint signals to give us an understanding of what is going on. If we see a flow that is all by itself it is probably not a big deal but if we see that same flow and it is connected to a suspicious login or a change, in how a process is behaving then it becomes a big deal. This helps us avoid wasting time on things that’re not important and focus on the things that really matter which reduces false positives and helps us investigate the right things.
Visibility across hybrid environments
Modern NBM solutions ingest telemetry from on-prem taps, cloud VPC flow logs, and host agents when needed. This unified view helps you identify cross-environment patterns — for example, a cloud VM contacting an internal file store in a way that deviates from known baselines.
What should you expect from an effective NBM or NDR solution?
Full protocol and port coverage
A good solution checks all the ports and protocols not the main ones so it can find threats that use unusual channels or hidden protocols. This means it looks really closely at everything. That helps stop attackers from sneaking into the traffic that people do not watch closely. A good solution, like this one checks all the ports and protocols to keep you safe.
Machine learning that understands how people behave is really useful for companies like enterprises. This kind of machine learning is specially made for the enterprise context. The goal of behavioral machine learning in an enterprise context is to make it work well for those companies. Behavioral machine learning is about making sense of what people do and that is very important, for enterprises.
We need to use machine learning in a way. Machine learning should be used with context not as a way to give someone a score without knowing how it works. We should have models that think about the workings of things the risk of assets and what happened in the past. This way when we get alerts they will actually make sense. Be, in order of what will affect our business the most. Machine learning like this will help us understand what is going on and make decisions.
Actionable findings and remediation workflows
Detection is not very useful if you do not do anything about it. This just wastes the time of the Security Operations Center team.
The solution should show us what is really going on and explain it in a way that makes sense. It should also give us some steps to follow or automate the process so we can stop behavior, on the Security Operations Center systems quickly.
The Security Operations Center team needs to be able to contain or block this behavior soon as possible.
Integration with your security stack
You have to connect Network Behavior Monitoring to the rest of your security tools. This is really important for getting the most out of Network Behavior Monitoring. It should work well with things like EDR, SIEM, XDR and other security tools to keep your network safe.
Network Behavior Monitoring helps by looking at all the alerts, from these tools and figuring out what is really going on. It gets information from the endpoints, identities and cloud to make sure it can detect problems correctly and not give you warnings.
Network Behavior Monitoring and all these other tools work together to keep your network safe. This bidirectional flow accelerates investigations, enabling analysts to move from signal to verdict faster, while also reducing alert fatigue through consolidated workflows and automated response actions. Ultimately, a tightly integrated stack ensures adversaries cannot exploit gaps between tools, turning NBM into a force multiplier that drives faster, smarter, and more decisive security outcomes.
How do you prioritize NBM alerts to avoid alert fatigue?
Risk-based scoring tied to asset criticality
Not every anomaly is critical. Prioritize alerts that involve high-value assets, privilege changes, or data exfiltration patterns so your team focuses on what threatens the business most.
Combine signal types for stronger evidence
Bump priority when multiple signals line up — for instance, anomalous network behavior plus abnormal authentication and unusual process activity — so you avoid chasing harmless deviations.
Use guided investigation and playbooks
Provide analysts with context, suggested next steps, and automated enrichment (who, what, when, where) so you can close the loop faster without manual data gathering.
Continuous tuning and feedback loops
Feed investigation outcomes back into your detection models so the system improves over time and fewer false positives reach analysts.
How do you measure NBM effectiveness and ROI?
Detection lead time and dwell time reduction
Track how much earlier NBM identifies suspicious behavior compared with legacy tools and measure reductions in attacker dwell time — shorter dwell time correlates directly to less data loss and lower incident cost.
Mean time to investigate (MTTI) and mean time to contain (MTTC)
If NBM provides richer context and fewer false positives, your investigation and containment times should fall — those are concrete operational gains you can measure.
Reduction in manual triage and incident volume
Quantify how many alerts are auto-prioritized or resolved with playbooks; fewer manual triage hours equals headcount savings or freed capacity for proactive work.
Business-aligned risk reduction
Map detected incidents and prevented exfiltration attempts to business impact (sensitive assets protected, compliance obligations met) to show executive ROI.
How does Fidelis Security help close these network visibility blind spots?
Deep session inspection and full protocol visibility
Fidelis Network (NDR product) emphasizes deep session inspection across all ports and protocols, giving you the ability to inspect and classify data in motion so you can detect exfiltration and hidden channels that standard tools might miss.
Behavioral machine learning and contextual anomaly detection
Fidelis documents a behavioral ML framework that analyzes multiple contexts (external, internal, data movement, application protocol, and events) to surface anomalies at scale and reduce noise. That contextual approach helps you prioritize findings that matter to your environment.
Correlation with deception and richer timelines
Fidelis integrates deception and NDR signals so when an attacker touches a deceptive asset, Fidelis correlates that interaction with network traffic and builds an attack timeline. That kind of correlation supplies high-confidence indicators that help you rapidly scope and contain intrusions.
Purpose-built XDR for unified visibility and faster response
Fidelis Elevate combines network, endpoint, cloud, and deception signals in an open XDR architecture so you get unified visibility and automated workflows that reduce detection-to-contain times. Fidelis highlights measurable gains in detection and response velocity for customers.
If you’re struggling with blind spots from encrypted traffic, slow exfiltration, or hybrid gaps, Fidelis’ emphasis on deep session inspection, behavioral ML, deception correlation, and XDR integration addresses those exact problems by giving you richer evidence, higher confidence alerts, and faster paths to containment. Schedule a demo to see how network behavior monitoring and Fidelis solutions reveal the blind spots you don’t yet see.
See why security teams trust Fidelis to:
Cut threat detection time by 9x
Simplify security operations
Provide unmatched visibility and control
The post Why Network Security Blind Spots Persist and How Behavior Monitoring Fixes Them appeared first on Fidelis Security.
No Responses