Modern attacks don't always look like attacks.
An attacker can use valid credentials, legitimate administrative tools, normal network protocols, and compromised infrastructure without immediately triggering a traditional malware alert.
That is one of the reasons I've become increasingly interested in Network Detection and Response (NDR).
NDR isn't simply about watching packets or generating more alerts. At its best, it's about understanding how systems communicate, what normal behavior looks like, and when those relationships start to indicate an attack.
What exactly is NDR?
Network Detection and Response is a security approach that continuously analyzes network activity to identify suspicious behavior, investigate potential threats, and support response.
Depending on the implementation, NDR can analyze signals such as:
- Network flows
- DNS activity
- IP addresses and destinations
- Protocol behavior
- Connection patterns
- East-west traffic
- North-south traffic
- Data transfer behavior
- Command-and-control communication
- Lateral movement
- Network anomalies
But collecting this information isn't the difficult part.
The more interesting question is:
What can you actually understand from it?
For example, imagine an internal server suddenly starts communicating with an unfamiliar external destination.
That event alone might not be enough to call it malicious.
Now add some context:
- A privileged account recently authenticated to the server.
- The server begins communicating with several internal systems.
- Outbound traffic increases significantly.
- The destination has a poor reputation.
- The endpoint is exhibiting unusual behavior. Individually, these events might not look particularly important. Together, they tell a very different story. That is where I think modern NDR becomes much more valuable.
NDR is about behavior, not just signatures
Traditional security controls remain extremely important.
Firewalls, IDS/IPS, endpoint security, access controls, segmentation, and other preventive technologies all have important roles.
But attackers have become better at operating inside the boundaries of legitimate activity.
An attacker doesn't necessarily need to deploy an obvious piece of malware.
They might instead:
- Obtain valid credentials.
- Authenticate to an internal system.
- Perform reconnaissance.
- Move laterally.
- Access sensitive resources.
- Establish command-and-control communication.
- Exfiltrate data.
The network activity surrounding these actions can provide valuable detection signals.
This is why behavioral analytics matters.
Instead of asking only:
"Does this traffic match a known malicious signature?"
we can also ask:
"Is this behavior normal for this user, device, application, or environment?"
That shift is important.
How NDR works
A typical NDR workflow can be simplified into five stages.
1. Collect network telemetry
NDR starts by collecting relevant network data.
This can include:
- Network flows
- DNS queries
- Firewall events
- Proxy activity
- Network device logs
- Authentication events
- Application traffic
- Cloud network telemetry
The objective isn't simply to collect more data.
It's to collect the right data that can provide security context.
2. Establish behavioral patterns
The platform analyzes activity such as:
- Connection frequency
- Communication destinations
- Protocol usage
- Traffic volume
- Timing
- Device relationships
- User behavior
- Historical activity
Over time, these signals help establish what normal behavior looks like.
3. Detect anomalies and threats
Detection can combine several techniques:
- Threat intelligence
- Signature-based detection
- Anomaly detection
- Behavioral analytics
- Machine learning
- Statistical analysis
- Event correlation
This combination is particularly useful when dealing with attacks that don't have an obvious signature.
4. Investigate
Detection is only the beginning.
A security analyst still needs to answer:
What happened?
Which systems were involved?
When did it begin?
Is this an isolated event?
Could this be part of a larger attack chain?
This is where network context becomes extremely useful.
5. Respond
Depending on the architecture and integrations, response can include:
- Blocking malicious traffic
- Blocking suspicious destinations
- Isolating endpoints
- Disabling compromised accounts
- Escalating incidents
- Triggering automated playbooks
- Enforcing security policies
This is also where NDR can connect with broader SIEM, XDR, SOAR, and endpoint security workflows.
NDR vs. EDR: Why both can matter
One of the easiest ways to understand NDR is to compare it with EDR.
EDR focuses primarily on the endpoint.
It can tell you about:
- Processes
- Files
- Applications
- Registry activity
- Endpoint behavior
- User activity
NDR focuses primarily on network communication.
For example:
EDR might tell you:
A suspicious process is running on a workstation.
NDR might tell you:
That workstation is communicating with an unusual external destination and attempting connections to multiple internal systems.
Put the two together and you have much more context.
This is why I don't see NDR and EDR as competing technologies.
They answer different questions.
NDR vs. XDR
XDR takes the concept further.
Instead of focusing primarily on network activity, XDR can correlate signals from:
- Endpoints
- Networks
- Cloud environments
- Identity systems
- Applications
- Other security tools
In that model, NDR becomes an important source of telemetry within a broader detection and response architecture.
The important part isn't the acronym.
It's the correlation.
Why SIEM + NDR can be powerful
SIEM platforms are designed to collect and correlate security events and logs across an environment.
NDR provides deeper visibility into network activity and communication behavior.
Consider a simplified example:
SIEM:
A privileged user authenticated at 2:15 AM.
EDR:
The user's workstation launched an unusual process.
NDR:
The workstation then started communicating with several previously unseen internal systems.
Threat intelligence:
One external destination associated with the activity has a known malicious reputation.
Individually, each signal may generate an alert.
When correlated, they can represent a much stronger indication of compromise.
This is the direction I think security operations are moving toward:
from isolated alerts → toward correlated attack context.
The importance of lateral movement
One area where network visibility is particularly valuable is lateral movement.
Once attackers compromise an initial system, they often don't stop there.
They may attempt to discover:
- Other hosts
- Servers
- Credentials
- Applications
- Administrative systems
- High-value databases
Unusual east-west communication can therefore become an important detection signal.
For example, if a workstation that normally communicates with three internal services suddenly begins attempting connections to dozens of systems, that deserves investigation.
It doesn't automatically mean compromise.
But it creates a useful security question:
Why did the communication pattern change?
NDR in cloud environments
The traditional idea of a network perimeter has changed significantly.
Organizations now operate across:
- AWS
- Azure
- Google Cloud
- SaaS applications
- Containers
- APIs
- Serverless workloads
- Remote users
- Hybrid infrastructure
This creates another challenge:
A huge amount of network activity is legitimate.
Cloud environments can generate enormous volumes of communication between services.
The challenge is therefore not simply seeing traffic.
It's distinguishing expected communication from meaningful deviations.
That makes behavioral analysis and contextual correlation increasingly important.
NDR and AI/ML
AI and machine learning can help NDR platforms analyze large volumes of network telemetry and identify patterns that would be difficult to evaluate manually.
Potential signals include:
- Device behavior
- User behavior
- Communication relationships
- Connection frequency
- Traffic volume
- Access patterns
- Destination reputation
- Historical activity
- Threat intelligence
Consider a server that normally communicates with a known set of services.
One day it starts:
- Communicating with an unfamiliar external destination.
- Sending substantially more data than usual.
- Accessing several internal systems.
- Showing unusual authentication activity.
No single event necessarily proves an attack.
But the behavioral combination becomes much more interesting.
This is where AI/ML-driven correlation can help security teams prioritize what deserves attention.
Where Seceon fits into this architecture
From my perspective, one of the more interesting aspects of Seceon's approach is that NDR isn't treated as an isolated security product.
Seceon's Open Threat Management (OTM) Platform brings together capabilities including SIEM, XDR, UEBA, SOAR, threat intelligence, threat hunting, and network visibility.
The underlying idea is straightforward:
Network + Endpoint + Identity + Cloud + Application + Threat Intelligence
should provide more context than any one of those signals operating independently.
For example, detecting a suspicious network connection is useful.
But being able to associate that connection with:
- An unusual login
- A compromised endpoint
- Privilege escalation
- Abnormal application behavior
- Known malicious infrastructure
- Lateral movement
- Unusual data transfers can make the investigation considerably more meaningful.
Seceon's OTM architecture is designed around this type of cross-domain correlation.
What should you look for in an NDR platform?
If you're evaluating NDR solutions, I would look beyond the marketing label.
Some capabilities worth evaluating include:
- Real-time network monitoring
- Network flow analysis
- Behavioral analytics
- Threat intelligence integration
- Machine learning
- Threat hunting
- Automated response
- SIEM integration
- XDR integration
- Cloud visibility
- Identity context
- Scalability
I'd also ask a practical question:
How much context does the platform provide when it raises an alert?
Generating an alert is relatively easy.
Helping an analyst understand why the alert matters is much more valuable.
NDR implementation: where teams can go wrong
Deploying NDR isn't simply a matter of installing a product and waiting for detections.
A few practical considerations matter.
Start with critical assets
Don't try to solve every visibility problem simultaneously.
Start with the network segments, systems, and assets where visibility matters most.
Establish baselines
You need to understand normal communication before abnormal behavior can be meaningful.
Integrate existing tools
NDR becomes more useful when it can work with existing:
- SIEM
- EDR
- Firewalls
- Identity systems
- Threat intelligence
- SOAR
Prioritize risk
Not every anomaly is an incident.
Security teams need ways to distinguish unusual activity from activity that represents meaningful risk.
Automate carefully
Automated containment can dramatically reduce response time, but high-impact actions should have appropriate conditions and safeguards.
My biggest takeaway
The biggest takeaway for me is that network visibility shouldn't exist in a silo.
NDR is valuable because network behavior can reveal things that aren't obvious from an endpoint, identity, or log perspective.
But its real potential appears when those signals are correlated.
A suspicious connection becomes more interesting when you know:
who initiated it → from which device → using which account → toward which destination → after what activity → with what data transfer → and what happened next.
That's the difference between simply monitoring a network and actually understanding an attack.
Final thoughts
Modern security operations aren't dealing with isolated endpoints and isolated alerts anymore.
They're dealing with interconnected environments spanning:
users + endpoints + networks + cloud + applications + identities + data.
NDR provides an important layer of visibility into those relationships.
And as attackers increasingly use legitimate credentials, cloud services, administrative tools, and techniques that don't necessarily match known signatures, behavioral and contextual detection will become increasingly important.
For me, the future of NDR isn't simply "more network monitoring."
It's:
better context + behavioral analytics + AI/ML + threat intelligence + cross-domain correlation + faster response.
That's where NDR can become much more than another security dashboard—it can become an important part of understanding the attack story.
This article is an adapted version of content originally published by Seceon. I work with Seceon and have added my own perspective on NDR and modern security operations.
Original article: https://seceon.com/network-detection-and-response/
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