Network visibility

Network Visibility


full stack observability


Network Visibility

In today’s interconnected world, where data flows at an unprecedented scale, network visibility plays a pivotal role in ensuring the smooth operation of organizations. It empowers businesses to gain insights, identify bottlenecks, and enhance network performance. This blog post will explore network visibility, its significance, benefits, and implementation strategies.

Network visibility refers to monitoring and analyzing network traffic, allowing organizations to understand their network infrastructure comprehensively. Network administrators can detect anomalies, troubleshoot issues, and optimize network performance by capturing and analyzing data packets. This visibility acts as a foundational element for effective network management.


Highlights: Network Visibility

  • Network Visibility Tools

The traditional network visibility tools give you the foundational data to see what’s going on in your network. Network visibility solutions are familiar, and network visibility tools such as NetFlow and IPFIX have been around for a while. However, they give you an incomplete part of the landscape. Then, we have a new way of looking with a new practice of distributed systems observability.

  • Observability

Observability software engineering brings a different context to the meaning of the data, allowing you to examine your infrastructure and its applications from other and more exciting angles. It combines traditional network visibility with a platform approach, enabling robust analysis and visibility with full-stack microservices observability.


Related: Before you proceed, you may find the following posts helpful:

  1. Observability vs. Monitoring
  2. Reliability In Distributed Systems
  3. WAN Monitoring
  4. Network Traffic Engineering
  5. SASE Visibility


Network Visibility Solutions

Key Network Visibility Discussion points:

  • The challenges with monitoring distributed systems.

  • Observability vs monitoring

  • Starting network visibility.

  • Network visibility tools.

  • Network visibility solutions.

  • Multilayer machine learning.


Back to basics: Network Visibility

The Role of Network Security

Your network and valuable assets are under internal and external threats, ranging from disgruntled employees to worldwide hackers. There is no perfect defense because hackers can bypass, compromise, or evade almost every safeguard, countermeasure, and security control. In addition, bad actors are continually creating new attack techniques, writing new exploits, and discovering new vulnerabilities.

Some essential security aspects stem from understanding bad actors’ strategies, methods, and motivations. You can anticipate future attacks once you learn to think like a hacker. This lets you devise new defenses before a hacker can breach your organization’s network.

Understanding Network Visibility

Network visibility refers to gaining clear insights into the network infrastructure, traffic, and the applications running on it. It involves capturing, monitoring, and analyzing network data to obtain valuable information about network performance, user behavior, and potential vulnerabilities. By having a comprehensive network view, organizations can make informed decisions, troubleshoot issues efficiently, and proactively address network challenges.

Network Visibilty Tools

Main Network Visibility Components

Network Visibility

  • Network visibility relies on robust traffic monitoring tools that capture and analyze network packets in real-time.

  • Network taps are hardware devices that provide a non-intrusive way to access network traffic.

  • Network packet brokers act as intermediaries between network taps and monitoring tools.

  • Packet capture tools capture network packets and provide detailed insights.

  • Flow-based monitoring tools collect information on network flows.

Key Components of Network Visibility

a) Traffic Monitoring: Effective network visibility relies on robust traffic monitoring tools that capture and analyze network packets in real-time. These tools provide granular details about network performance, bandwidth utilization, and application behavior, enabling organizations to identify and resolve bottlenecks.

b) Network Taps: Network taps are hardware devices that provide a non-intrusive way to access network traffic. Organizations can gain full visibility into network data by connecting to a network tap without disrupting network operations. This ensures accurate monitoring and analysis of network traffic.

c) Network Packet Brokers: Network packet brokers act as intermediaries between network taps and monitoring tools. They collect, filter, and distribute network packets to the appropriate monitoring tools, optimizing traffic visibility and ensuring efficient data analysis.

d) Packet Capture and Analysis:

Packet capture tools capture network packets and provide detailed insights into network behavior, protocols, and potential issues. These tools enable deep packet inspection and analysis, facilitating troubleshooting, performance monitoring, and security investigations.

e) Flow-Based Monitoring:

Flow-based monitoring tools collect information on network flows, including source and destination IP addresses, protocols, and data volumes. By analyzing flow data, organizations can gain visibility into network traffic patterns, identify anomalies, and detect potential security threats.

A key point: Lab on tcpdump

Capturing Traffic: Network Analysis

Tcpdump is a command-line packet analyzer tool that allows you to capture and analyze network packets. It captures packets from a network interface and displays their contents in real time or saves them to a file for later analysis. With tcpdump, you can inspect packet headers, filter packets based on specific criteria, and perform detailed network traffic analysis.


  1. Run tcpdump -D. This should show you the available interfaces to collect packet data.

  2. Run sudo tcpdump -i ens33 -s0 -w sample.pcap. This command does the following:

    • Captures packets coming from and going to the interface (-i) ens5

    • Sets the snaplengh (-s) to the maximum size. You may specify a number if you want to reduce the size of the packet being captured intentionally.

    • Writes (-w) the capture into a packet capture (pcap) format.

  3. Open a web browser and visit to generate some IP traffic.



Tcpdump finds its applications in various scenarios. One everyday use case is network troubleshooting, where administrators can capture and analyze packets to identify network issues such as latency, packet loss, or misconfigurations. Another use case is network security analysis, where tcpdump can help detect and investigate malicious activities, such as suspicious network traffic or potential intrusion attempts. Furthermore, tcpdump can be used for network performance monitoring, protocol debugging, and even educational purposes.


To make the most out of tcpdump, here are some tips and tricks:

– Utilize filters: Tcpdump allows you to apply filters based on source/destination IP addresses, ports, protocols, and more. Filters help focus on relevant packet captures and reduce noise.

– Save to file: By saving captured packets to a file, you can analyze them later or share them with colleagues for collaborative troubleshooting or analysis.

– Combine with other tools: Tcpdump can be used with network analysis tools like Wireshark for a more comprehensive analysis. Wireshark provides a graphical interface and additional features for in-depth packet inspection.


Tcpdump is a powerful and versatile tool for network packet analysis. Its ability to capture, filter, and analyze packets makes it invaluable for network administrators, security analysts, and anyone seeking to understand and troubleshoot network traffic. By leveraging tcpdump’s features and following best practices, you can gain valuable insights into your network and ensure its optimal performance and security.

Benefits of Network Visibility

a) Enhanced Performance Management: Network visibility enables organizations to monitor network performance metrics in real-time, such as latency, packet loss, and throughput. Organizations can promptly identify and address performance issues, optimize network resources, improve user experience, and reduce downtime.

b) Advanced Threat Detection: With the rise in sophisticated cyber threats, network visibility plays a crucial role in detecting and mitigating security breaches. Organizations can detect suspicious activities, unauthorized access attempts, and potential data exfiltration by analyzing network traffic patterns and anomalies.

c) Compliance and Regulatory Requirements: Many industries have strict compliance and regulatory requirements regarding data security and privacy. Network visibility helps organizations meet these requirements by providing visibility into data flows, ensuring secure transmission, and facilitating audit trails.

Implementing Network Visibility Strategies

a) Define Objectives: Organizations must identify specific network visibility objectives, such as improving application performance or enhancing security monitoring. Clear goals will guide the selection and implementation of appropriate network visibility solutions.

b) Choose the Right Tools: Organizations should evaluate and select the correct network visibility tools and technologies based on the defined objectives. This includes traffic monitoring tools, network taps, and network packet brokers that align with their requirements and infrastructure.

c) Integration and Scalability: Implementing network visibility solutions requires seamless integration with existing network infrastructure. Organizations should ensure compatibility and scalability to accommodate future growth and changing network dynamics.

A key point: Network Visibility with Cisco IOS

Visibility with CDP

Cisco CDP is a proprietary Layer 2 network protocol developed by Cisco Systems. It operates at the Data Link Layer of the OSI model and enables network devices to discover and gather information about other directly connected devices. By exchanging CDP packets, devices can learn about their neighbors, including device types, IP addresses, and capabilities.

The Benefits of Cisco CDP

a) Enhanced Network Visibility: Cisco CDP provides network administrators with real-time information about neighboring devices, enabling them to map and understand the network topology. This visibility helps identify potential points of failure, optimize network design, and troubleshoot issues promptly.

b) Simplified Network Management: With Cisco CDP, network administrators can quickly identify and track devices connected to the network. This simplifies device inventory management, configuration updates, and network change monitoring.

c) Improved Network Efficiency: Cisco CDP automatically reduces manual configuration efforts and minimizes human errors by automatically discovering neighboring devices. This leads to improved network efficiency, faster troubleshooting, and reduced downtime.

Use Cases for Cisco CDP

a) Network Troubleshooting: When network issues arise, Cisco CDP can be beneficial in identifying the root cause. Administrators can quickly isolate faulty devices or misconfigurations by revealing information about neighboring devices and their connections.

b) Network Design and Planning: During a network infrastructure’s design and planning phase, Cisco CDP assists in creating accurate network diagrams and understanding device interconnections. This information is valuable for optimizing network performance and capacity planning.

c) Security Auditing: Cisco CDP also plays a role in network security auditing. Administrators can ensure network integrity and mitigate potential security risks by identifying unauthorized devices or rogue switches.


Cisco CDP is a game-changer when it comes to network management and efficiency. With its ability to provide detailed information about neighboring devices, network administrators gain unparalleled visibility into their network topology. This enhanced visibility leads to improved troubleshooting, simplified management, and optimized network design. By leveraging the power of Cisco CDP, businesses can ensure a robust and reliable network infrastructure that meets their ever-evolving needs.


  • A key point: Security threats with network analysis and visibility

Remember, those performance problems are often a direct result of a security breach. So, distributed systems observability goes hand in hand with networking and security. It does this by gathering as much data as possible, commonly known as machine data, from multiple data points. It then ingests the data and applies normalization and correlation techniques with some algorithm or statistical model to derive meaning.  

network visibility tools
Diagram: The challenges of network visibility tools.


Starting Network Visibility

Network visibility solutions

Combating the constantly evolving threat actor requires good network analysis and visibility along with analytics into all areas of the infrastructure, especially the host and user behavior aligning with the traffic flowing between hosts. This is where machine learning (ML) and multiple analytical engines detect and respond to suspicious and malicious activity in the network.

All of which is done against machine data that multiple tools have traditionally gathered is stored in separate databases. Adding content to previously unstructured data will allow you to extract all sorts of valuable insights, which can be helpful for security, network performance, and user behavior monitoring.

System observability and data-driven visibility

The big difference between traditional network visibility and distributed systems observability is between seeing and understanding what’s happening in your network and, more importantly, understanding why it’s happening. This empowers you to get to the root cause more quickly. Be it a network or security-related incident. For all of this, we need to turn to data to find meaning, often called data-driven visibility in real-time, required to maximize positive outcomes while minimizing or eliminating issues before they happen.


Machine data and observability

Data-drive visibility is derived from machine data. So, what is machine data? Machine data is everywhere and flows from all the devices we interact with, making up around 90% of today’s data. And harnessing this data can give you powerful insights for networking and security. Furthermore,  machine data can be in many formats, such as structured and unstructured.

As a result, it can be challenging to predict and process. So when you find issues in machine data, you need to be able to fix them in less time. So, you need to be able to pinpoint, correlate, and alert specific events so we can save time. 

We need a platform that can perform network analysis and visibility instead of only using multiple tools dispersed throughout the network. A platform can take data from any device and create an intelligent, searchable index. For example, a SIEM solution can create a searchable index for you. There are several network visibility solutions, such as cloud-based or on-premise-based solutions. 

distributed systems observability
Diagram: Distributed systems observability and machine data.


Network Visibility Tools

Traditional, legacy, or network visibility tools are the data we collect with SNMP, network flows, and IPFIX, even from routing tables and geo-locations. To recap, IPFIX is an accounting technology that monitors traffic flows. IPFIX then interprets the client, server, protocol, and port used, counts the number of bytes and packets, and sends that data to an IPFIX collector.

Network flow or traffic is the amount of data transmitted across a network over a specific period. The flow identification is performed based on five fields in the packet header. These fields are the following: source I.P. address, destination I.P. address, protocol identifier, source port number, and destination port number.

Then, we have SNMP, a networking protocol for managing and monitoring network-connected devices. The SNMP protocol is embedded in multiple local devices. None of these technologies is going away; they must be correlated and connected.


Traditional network visibility tools:

  • Populate charts and create baselines

From this data, we can implement network security. First, we can create baselines, identify anomalies, and start to organize network activity. Alerts are triggered when thresholds are met. So we get a warning about a router that is down or an application is not performing as expected. This can be real-time or historical. However, this is all good for the previous way of doing things. But for example, when an application is not performing well, a threshold tells you nothing; you need to be able to see the full paths and any use of each part of the transaction.

All of which were used to populate the charts and graphs. These dashboards rely on known problems that we have seen in the past. However, today’s networks fail in creative ways often referred to as unknown/unknown, calling for a new approach to distributed systems observability that Site Reliability Engineering (SRE) teams employ.


Observability Software Engineering

To start an observability project, we need diverse data and visibility to see various things happening today. We don’t just have known problems anymore. We have a mix of issues that we have yet to see before. Networks fail in creative ways, some of which have never happened before. We need to look at the network differently with new and old network visibility tools and the practices of observability software engineering.

We need to diversify your data so we have multiple perspectives to understand better what you are looking at. And this can only be done with a distributed systems observability platform. What does this platform need?


Network analysis and visibility:

Multiple data types and point solutions

So, we need to get as much data as possible from all network visibility tools such as flows, SNMP, IPFIX, routing tables, packets, telemetry logs, metrics, logs, and traces. Of course, we are familiar with and have used everything in the past, and each data type provides a different perspective. However, the main drawback of not using a platform is that it lends itself to a series of point solutions, leaving gaps in network visibility.

Now we have a database of each one. So, we could have a database for network traffic flow information for application visibility or a database for SNMP. The issue with the point solution is that you can only see some things. Each data point acts on its island of visibility, and you will have difficulty understanding what is happening. At a bare minimum, you should have some automation between all these devices.


  • A key point: The use of automation as the starting point

Automation could be used to glue everything together. There are two variants of the Ansible architecture with a CLI version known as Ansible Core or a Platform-based approach with Ansible Tower. Automation does not provide visibility, but it is a starting point to glue together the different point solutions to increase network visibility.

For example, you are collecting all logs from all firewall devices and sending them to a backend for analysis. Ansible variables are recommended, and you can use the Ansible inventory variable to fine-tune how you connect to your managed assets. In addition, variables bring many benefits and modularity to Ansible playbooks.


distributed systems observability
Diagram: Distributed systems observability: The issue of point solutions.


Isolated monitoring for practical network analysis and visibility

I know what happens on my LAN, but what happens in my service provider networks.? I can see VPC flows from a single cloud provider, but what happens in my multi-cloud designs? I can see what is happening in my interface states, but what is happening in my overlay networks? 

For SD-WAN monitoring, if a performance problem with one of my applications or a bad user experience is reported from a remote office, how do we map this back to tunnels? We have pieces of information that are missing end-to-end pictures. For additional information on monitoring and visibility in SD-WAN environments, check out this SDWAN tutorial.

The issue without data correlation?

How do we find out if there is a problem when we have to search through multiple databases and dashboards? And when there is a problem, how do you correlate to determine the root cause? What if you have tons of logs and must figure out that this interface utilization correlates with this slow DNS lookup time, which links to a change in BGP configuration?

So you can see everything with traditional or legacy visibility, but how do you go beyond that? How do you know why something has happened? This is where distributed systems observability and the practices of observability software engineering come in—having full-stack observability with network visibility solutions into all network angles.


Distributed Systems Observability:

Seeing is believing

The difference between seeing and understanding. Traditional network visibility solutions let you see what’s happening on your networks. But on the other hand, observability helps you understand why it is happening. With observability, we are not replacing network visibility; we are augmenting this with a distributed systems observability platform that lets us combine all the dots to form a complete picture. With a distributed systems observability platform, we still collect the same information.

For example, routing information, network traffic, VPC flow logs, results from synthetic tests, metrics, traces, and logs. But now we have several additional steps of normalization and correlations that the platform takes care of for you.

Distributed systems observability and normalization

Interface statistics could be packet per second; flow data might be in the percentage of traffic, such as 10% is DNS traffic. Then, we have to normalize and correlate it to understand what happens for the entire business transaction. So, the first step is to ingest as much data as possible, identify or tag data, and then normalize the data. Keep in mind this could be short-lived data, such as interface statistics.

network visibility tools
Diagram: Connecting the dots with network visibility tools.


Applying machine learning algorithms

All these different types of data are ingested, normalized, and correlated. And this can not be done with a human. Distributed systems observability gives you practical, actionable intelligence that automates the root cause and measures network health by applying machine learning algorithms.

We will discuss these machine learning algorithms and statistically analyze them momentarily. Supervised and unsupervised machine learning is used heavily in the security world. So, in summary, for practical network analysis and visibility, we need to do the following:



Summary Point 1

We must inject a large amount of data from many sources and types

Summary Point 2

Automate baseline and anomaly detection and make this more accurate

Summary Point 3

Accurate group data and create a structured amount of unstructured data

Summary Point 4

Then, correlate data to learn how everything is related to each other


      • This will give you full stack observability for enhanced network visibility that traditional network visibility tools cannot give you.


Full Stack Observability

We’d like to briefly describe the transitions we have gone through and why we need to address full-stack observability. First, we had a monolithic application, which is still very alive today, and this is where the mission-critical system lives. We then moved to the cloud and started adopting containers and platforms. Then, there was a drive to re-architect the code and begin from the beginning with cloud-native and now with observability.

Finally, monitoring becomes more important with the move to containers and kubernetes. Why? Because the environments are dynamic, you need to embed security somehow.

The traditional world of normality

In the past, network analysis and visibility were simple. Applications ran in single private data centers, potentially two data centers for high availability. These data centers were on-premises, and all components were housed internally. 

In addition, the network and infrastructure were pretty static, and there were few changes to the stack, for example, daily. However, nowadays, we are in a different environment where we have complex and distributed applications. This is with components/services located in many other places and types of places, on-premises and in the cloud, depending on local and remote services. 

The wave of containers and its effect on the network analysis and visibility

There has been a considerable rise in the use of containers. The container wave introduces dynamic environments with cloud-like behavior where you can scale up and down very quickly and easily. We have temporary components. These things are coming up and down inside containers and are part of services.

The paths and transactions are both complex but also shifting. So you have multiple steps or services for an application: A business transaction. It would be best if you strived to have the automatic discovery of business transactions and application topology maps of how the traffic flows.

The wave of Microservices and its effect on network analysis and visibility

With the wave towards microservices, we get the benefits of scalability and business continuity, but managing is very complex. In addition, what used to be method calls or interprocess calls within the monolith host now go over the network and are susceptible to deviations in latency. 

The issue of silo-based monitoring

With all these new waves of microservices and containers, we have an issue in silo monitoring with poor network analysis and visibility in a very distributed environment. Let us look at an example of isolating a problem with traditional network visibility and monitoring.

For mobile or web, the checkout is slow; for the application, there could be JVM perf issues. Then, on the database, we could have a slow SQL query; on the network side, we have an interface rate of 80%. So traditional network visibility and monitoring with a silo-based approach have their tools, but something needs to be connected; how do you quickly get to the root cause of this problem?


Network visibility solutions

When you look at monitoring, it’s based on event alerts and the dashboard. All of which is populated with passive ( sampling ) to generate a dashboard. It is also per domain. However, we have very complex, distributed, and hybrid environments.

We have a hybrid notion from a code perspective and physical location with cloud-native solutions. The way you consume API will be different, too, in each area. How you consume API for SaaS will differ for authentication for on-premise to that of the cloud. Keep in mind that API security is a top concern.

With our network visibility solutions, we must support all the journeys in a complex and distributed world. So we need system full-stack observability and observability software engineering to see what happens in each domain and to know what is happening in real time.

So, instead of being passive with data, we are active with metrics, logs, traces, events, and any other types of data we can inject. If there is a network problem, we inject all network-related data. Same with security, we inject all security-related information if there is a security problem.

Example: Getting hit by Malware 

If Malware hits you, you need to be able to detect a container quickly. Then, avoid remote code execution attempts from succeeding while putting the affected server in quarantine for patching.

So There are several stages you need to perform. And the security breach has affected you across different domains and teams. The topology now all changes, too. The backend and front end will change, so we must re-route traffic while keeping the performance. To solve this, we need to analyze different types of data.


The different data types

So you need to inject as much telemetry data as possible: application, security, VPC, VNETs, and Internet statistics. So, we get all this data created via automation, metrics, events, logs, and distributed tracing based on open telemetry.

  • Metrics: Metrics are aggregated measurements grouped or collected at the standard interface or a given period. For example, there could be a 1 min aggregate, so some details are lots. Aggregation helps you save on storage but requires proper pre-planning on what metrics to consider.
  • Events: Events are discrete actions happening at the moment in time. The more metadata associated with an event, the better. Events are helpful to confirm that particular actions occurred at a specific time. 
  • Logs: Logs are detailed and have timestamps associated with them. These can either be structured or unstructured. As a result, logs are very versatile and empower many use cases.
  • Traces: Traces are events that change between different application components. This item was purchased via credit cards at this time; it took 37 seconds to complete the transactions. All chain details and dependencies are part of the trace. Traces allow you to follow what is going on.

In the case of Malware detection. This is where a combination of metrics, traces, and logs would have helped you, and switching between views and having automated correlation will help you get to the root cause. But you must also detect and respond appropriately, leading us to secure network analytics.


Secure Network Analytics

We need good, secure network analytics for visibility and detection and then respond best. We have several different types of analytical engines that can be used to detect a threat. In the last few years, we have seen an increase in the talk and drive around analytics and how it can be used in networking and security. Many vendors claim they do both supervised and unsupervised machine learning. All of which are used in the detection phase.

distributed systems observability
Diagram: Distributed Systems Observability and issues of point solutions.


Algorithms and statistical models

For analytics, we have algorithms and statistical models. The algorithms and statistical models aim to achieve some outcome and are extremely useful in understanding domains constantly evolving with many variations.  This is precisely what the security domain is, by definition.

However, the threat landscape is growing daily, so if you want to find these threats, you need to shift through a lot of data, commonly known as machine data, that we discussed at the start of the post.

For supervised machine learning, we get a piece of Malware and build up a threat profile that can be gleaned from massive amounts of data. So when you see a matching behavior profile for that, you can make an alarm. But you need a lot of data to start with.

Crypto mining

This can capture very evasive threats such as crypto mining. A cryptocurrency miner is a software that uses your computer resources to mine cryptocurrency. A crypto mining event of the current miner is just a long-lived flow. It would be best if you had additional ways to determine or gather more metrics to understand that this long-lived flow is malicious and is a cryptocurrency miner.

full stack obervabiliity
Diagram: Full stack observability will capture crypto mining.


Multilayer Machine Learning Model

By their nature, crypto mining and even Tor will escape most security controls. To capture these, you need a multilayer machine learning model of supervised and unsupervised. So, if you are on a standard network that blocks Tor, it will stop 70% of the time; the other 30% of the entry and exit nodes are unknown.

Machine Learning (ML)

Supervised and unsupervised machine learning gives you the additional visibility to find those unknown / unknowns. The unique situations that are lurking on your networks. So here we are making an observation, and these models will help you understand whether these are not normal. There are different observation triggers.

First, there is the known bad behavior – such as security policy violation and communication to known C&C. Then we have anomaly conditions, which are the observed behavior that is different from usual. And we need to make these alerts meaningful to the business.


network visibility tools
Diagram: Full stack observability with a layered approach.


Meaningful alerts

If I.P. addresses, upload a large amount of data. It should say that the PCI server is uploading a large amount of data to a known malicious external network, and these are the remediation options. The statement or alert needs to mean something to the business.

We need to express the algorithms in the language of the company. This host could have a behavior profile that does not expect it to download or upload anything. 

Augment Information

When events leave the system, you can enrich it with data from other systems. You can enhance data inputs with additional telemetry to improve data with other sources that give it more meaning. To help with your alarm, you can add information to the entity. There’s a lot of telemetry in the network. Most devices support NetFlow and IPFIX; you can have Encrypted Traffic Analyses (ETA) and Deep Packet Inspection (DPI).

encrypted traffic analysis
Diagram: Encrypted traffic analyses.


So you can get loads of valuable insights from these different types of, let’s say, technologies. You can get usernames, device identities, roles, pattern behavior, and locations for additional data sources here. ETA can get a lot of information just by looking at the header without performing decryption. So you can enhance your knowledge of the entity with additional telemetry data. 

Network analysis and visibility with a tiered alarm system

Once an alert is received, you can create actions such as sending a Syslog message, email, SMTP trap, and webhooks. So you have a tiered alarm system with different priorities and severity on alarms. Then, you can enrich or extend the detection with data from other products. It can query other products via their API, such as Cisco Talos.

Instead of presenting all the data, they must give them the data they care about. This will add context to the investigation and help the overworked security analyst who is spending 90 mins on one Phishing email investigation.


Summary: Understanding Network Visibility

Network visibility refers to real-time monitoring, analyzing, and visualizing network traffic, data, and activities. It provides a comprehensive view of the entire network ecosystem, including physical and virtual components, devices, applications, and users. Organizations gain valuable insights into network performance bottlenecks, security threats, and operational inefficiencies by capturing and processing network data.

The Benefits of Network Visibility

Enhanced Network Performance: Organizations can proactively identify and resolve performance issues with network visibility. They can optimize network resources, ensure smooth operations, and improve user experience by monitoring network traffic patterns, bandwidth utilization, and latency.

Strengthened Security Posture: Network visibility is a powerful security tool, enabling organizations to detect and mitigate potential threats in real time. By analyzing traffic behavior, identifying anomalies, and correlating events, businesses can respond swiftly to security incidents, safeguarding their digital assets and sensitive data.

Improved Operational Efficiency: Network visibility provides valuable insights into network usage, allowing organizations to optimize resource allocation, plan capacity upgrades, and streamline network configurations. This results in improved operational efficiency, reduced downtime, and cost savings.

Implementing Network Visibility Solutions

Network Monitoring Tools: Deploying robust monitoring tools is essential for achieving comprehensive visibility. These tools collect and analyze network data, generating detailed reports and alerts. Various monitoring techniques can be employed to suit specific organizational needs, from packet sniffing to flow-based analysis.

Traffic Analysis and Visualization: Network visibility solutions often include traffic analysis and visualization capabilities, enabling organizations to gain actionable insights from network data. These visual representations help identify traffic patterns, trends, and potential issues at a glance, simplifying troubleshooting and decision-making processes.

Real-World Use Cases

Network Performance Optimization: A multinational corporation successfully utilizes network visibility to identify bandwidth bottlenecks and optimize network resources. By monitoring traffic patterns, they could reroute traffic and implement Quality of Service (QoS) policies, enhancing network performance and improving user experience.

Security Incident Response: A financial institution leverages network visibility to detect and respond to cybersecurity threats swiftly. By analyzing network traffic in real time, they identified suspicious activities and potential data breaches, enabling them to take immediate action and mitigate risks effectively.

Conclusion: Network visibility is no longer a luxury but a necessity for businesses operating in today’s digital landscape. It empowers organizations to proactively manage network performance, strengthen security postures, and improve operational efficiency. By implementing robust network visibility solutions and leveraging the insights they provide, businesses can unlock the full potential of their digital infrastructure.


network visibility tools


Matt Conran: The Visual Age
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