Skip to main content

Network Forensics at 40G and 100G Speeds

Mandana Javaheri

The 40G and 100G market will generate tens of billions of dollars in revenue in the next few years according to a recent Infonetics market forecast. Growth in traffic, which some analysts estimate will reach 50 to 60 percent annually, enables new opportunities but also puts enormous pressure on networks and creates new challenges.

Network forensics is one of these new challenges. Although network forensics is most commonly associated with investigating security incidents and breaches, it is also very valuable for providing visibility into network activities, troubleshooting issues quickly and diagnosing common network problems such as connectivity, unexpected change in utilization, or poor VoIP call quality.

Here are some of the ways you can prepare for successful network forensics as network speeds increase.

Know your Network

To identify anomalies, first you need to define or benchmark what is "normal" for your network. Your network performance solution is your best friend here. Baselining key business applications as well as measuring important network-based metrics such as packet size distribution, protocol and node usage will build an accurate model to know the normal behavior so you have something to compare to in case of problems.

Prepare for Everything

It is not just about having the right network forensics solution; you need the right infrastructure for your new, fast network as well. From your switches to your routers to your network packet brokers to your filtering criteria to your monitoring and forensics tools, everything has to be fast-speed compatible.

And most importantly you need to know your network and ask yourself the right questions:

What is your strategy?

Does it make sense to load-balance your traffic across multiple network forensics devices to get the full visibility?

Does it make sense to filter out the traffic you don't need?

What is your use case?

How do you usually find out there is an issue?

Is it by constantly monitoring the network or by receiving trouble tickets about performance?

Every network has its own specific needs, so make sure you know what those needs are and pick a network forensics partner that will help you meet them.

Smart Storage

One of the important components of making sure you have the network level data available to you when needed is defining the storage requirements. The faster the network becomes, the more storage is required to store what you need.

A fully utilized 1G network will generate 11TB of data per day. To control storage costs, you will need to get smarter about what is stored. This is only possible by knowing the network and your specific use cases. Techniques like filtering, packet slicing and load-balancing will help you use your storage more efficiently, while extended storage, SAN, and cloud-based technologies are also available if needed.

Depending on your network traffic, forensics and storage requirements, you should pick the amount and type of storage you require today and make sure it can scale to meet your needs in the future.

Intelligent Forensics

Searching through large amounts of packet data to find that essential little trace can be a frustrating process. So pick your search criteria and the type of analytics you need to run on your traffic wisely. Use your knowledge about the network baseline to define the forensics criteria. Make your search as focused as possible using filters. Define the time range, the application, the server or client which is experiencing the issue and drill down to as much detail as needed for troubleshooting. For example, if your problem is not VoIP or wireless related, don't use hardware resources to analyze those.

By knowing your network, using the right techniques and planning ahead, you can turn 40G and 100G network challenges into new opportunities.

Mandana Javaheri is CTO of Savvius.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

Network Forensics at 40G and 100G Speeds

Mandana Javaheri

The 40G and 100G market will generate tens of billions of dollars in revenue in the next few years according to a recent Infonetics market forecast. Growth in traffic, which some analysts estimate will reach 50 to 60 percent annually, enables new opportunities but also puts enormous pressure on networks and creates new challenges.

Network forensics is one of these new challenges. Although network forensics is most commonly associated with investigating security incidents and breaches, it is also very valuable for providing visibility into network activities, troubleshooting issues quickly and diagnosing common network problems such as connectivity, unexpected change in utilization, or poor VoIP call quality.

Here are some of the ways you can prepare for successful network forensics as network speeds increase.

Know your Network

To identify anomalies, first you need to define or benchmark what is "normal" for your network. Your network performance solution is your best friend here. Baselining key business applications as well as measuring important network-based metrics such as packet size distribution, protocol and node usage will build an accurate model to know the normal behavior so you have something to compare to in case of problems.

Prepare for Everything

It is not just about having the right network forensics solution; you need the right infrastructure for your new, fast network as well. From your switches to your routers to your network packet brokers to your filtering criteria to your monitoring and forensics tools, everything has to be fast-speed compatible.

And most importantly you need to know your network and ask yourself the right questions:

What is your strategy?

Does it make sense to load-balance your traffic across multiple network forensics devices to get the full visibility?

Does it make sense to filter out the traffic you don't need?

What is your use case?

How do you usually find out there is an issue?

Is it by constantly monitoring the network or by receiving trouble tickets about performance?

Every network has its own specific needs, so make sure you know what those needs are and pick a network forensics partner that will help you meet them.

Smart Storage

One of the important components of making sure you have the network level data available to you when needed is defining the storage requirements. The faster the network becomes, the more storage is required to store what you need.

A fully utilized 1G network will generate 11TB of data per day. To control storage costs, you will need to get smarter about what is stored. This is only possible by knowing the network and your specific use cases. Techniques like filtering, packet slicing and load-balancing will help you use your storage more efficiently, while extended storage, SAN, and cloud-based technologies are also available if needed.

Depending on your network traffic, forensics and storage requirements, you should pick the amount and type of storage you require today and make sure it can scale to meet your needs in the future.

Intelligent Forensics

Searching through large amounts of packet data to find that essential little trace can be a frustrating process. So pick your search criteria and the type of analytics you need to run on your traffic wisely. Use your knowledge about the network baseline to define the forensics criteria. Make your search as focused as possible using filters. Define the time range, the application, the server or client which is experiencing the issue and drill down to as much detail as needed for troubleshooting. For example, if your problem is not VoIP or wireless related, don't use hardware resources to analyze those.

By knowing your network, using the right techniques and planning ahead, you can turn 40G and 100G network challenges into new opportunities.

Mandana Javaheri is CTO of Savvius.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.