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Visibility is Security

Keith Bromley

While security experts may disagree on exactly how to secure a network, one thing they all agree on is that you cannot defend against what you cannot see. In other words, network visibility IS network security.

Visibility needs to be the starting the point. After that, you can implement whatever appliances, processes, and configurations you need to finish off the security architecture. By adopting this strategy, IT will acquire an even better insight and understanding of the network and application performance to maximize security defenses and breach remediation.

One easy way to gain this insight is to implement a visibility architecture that utilizes application intelligence. This type of architecture delivers the critical intelligence needed to boost network security protection and create more efficiencies.

For instance, early detection of breaches using application data reduces the loss of personally identifiable information (PII) and reduces breach costs. Specifically, application level information can be used to expose indicators of compromise, provide geolocation of attack vectors, and combat secure sockets layer (SSL) encrypted threats.

You might be asking, what is a visibility architecture?

A visibility architecture is nothing more than an end-to-end infrastructure which enables physical and virtual network, application, and security visibility. This includes taps, bypass switches, packet brokers, security and monitoring tools, and application-level solutions.

Let's look at a couple use cases to see the real benefits.

Use Case #1 – Application filtering for security and monitoring tools

A core benefit of application intelligence is the ability to use application data filtering to improve security and monitoring tool efficiencies. Delivering the right information is critical because as we all know, garbage in results in garbage out.

For instance, by screening application data before it is sent to an intrusion detection system (IDS), information that typically does not require screening (e.g. voice and video) can be routed downstream and bypass IDS inspection. Eliminating inspection of this low-risk data can make your IDS solution up to 35% more efficient.

Use Case #2 – Exposing Indicators of Compromise (IOC)

The main purpose of investigating indicators of compromise for security attacks is so that you can discover and remediate breaches faster. Security breaches almost always leave behind some indication of the intrusion, whether it is malware, suspicious activity, some sign of other exploit, or the IP addresses of the malware controller.

Despite this, according to the 2016 Verizon Data Breach Investigation Report, most victimized companies don't discover security breaches themselves. Approximately 75% have to be informed by law enforcement and 3rd parties (customers, suppliers, business partners, etc.) that they have been breached. In other words, the company had no idea the breach had happened.

To make matters worse, the average time for the breach detection was 168 days, according to the 2016 Trustwave Global Security Report.

To thwart these security attacks, you need the ability to detect application signatures and monitor your network so that you know what is, and what is not, happening on your network. This allows you to see rogue applications running on your network along with visible footprints that hackers leave as they travel through your systems and networks. The key is to look at a macroscopic, or application view, of the network for IOC.

For instance, suppose there is a foreign actor in Eastern Europe (or other area of the world) that has gained access to your network. Using application data and geo-location information, you would easily be able to see that someone in Eastern Europe is transferring files off of the network from an FTP server in Dallas, Texas back to an address in Eastern Europe. Is this an issue? It depends upon whether you have authorized users in that location or not. If not, it's probably a problem.

Due to application intelligence, you now know that the activity is happening. The rest is up to you to decide if this is an indicator of compromise for your network or not.

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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 ...

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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 ...

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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.

Visibility is Security

Keith Bromley

While security experts may disagree on exactly how to secure a network, one thing they all agree on is that you cannot defend against what you cannot see. In other words, network visibility IS network security.

Visibility needs to be the starting the point. After that, you can implement whatever appliances, processes, and configurations you need to finish off the security architecture. By adopting this strategy, IT will acquire an even better insight and understanding of the network and application performance to maximize security defenses and breach remediation.

One easy way to gain this insight is to implement a visibility architecture that utilizes application intelligence. This type of architecture delivers the critical intelligence needed to boost network security protection and create more efficiencies.

For instance, early detection of breaches using application data reduces the loss of personally identifiable information (PII) and reduces breach costs. Specifically, application level information can be used to expose indicators of compromise, provide geolocation of attack vectors, and combat secure sockets layer (SSL) encrypted threats.

You might be asking, what is a visibility architecture?

A visibility architecture is nothing more than an end-to-end infrastructure which enables physical and virtual network, application, and security visibility. This includes taps, bypass switches, packet brokers, security and monitoring tools, and application-level solutions.

Let's look at a couple use cases to see the real benefits.

Use Case #1 – Application filtering for security and monitoring tools

A core benefit of application intelligence is the ability to use application data filtering to improve security and monitoring tool efficiencies. Delivering the right information is critical because as we all know, garbage in results in garbage out.

For instance, by screening application data before it is sent to an intrusion detection system (IDS), information that typically does not require screening (e.g. voice and video) can be routed downstream and bypass IDS inspection. Eliminating inspection of this low-risk data can make your IDS solution up to 35% more efficient.

Use Case #2 – Exposing Indicators of Compromise (IOC)

The main purpose of investigating indicators of compromise for security attacks is so that you can discover and remediate breaches faster. Security breaches almost always leave behind some indication of the intrusion, whether it is malware, suspicious activity, some sign of other exploit, or the IP addresses of the malware controller.

Despite this, according to the 2016 Verizon Data Breach Investigation Report, most victimized companies don't discover security breaches themselves. Approximately 75% have to be informed by law enforcement and 3rd parties (customers, suppliers, business partners, etc.) that they have been breached. In other words, the company had no idea the breach had happened.

To make matters worse, the average time for the breach detection was 168 days, according to the 2016 Trustwave Global Security Report.

To thwart these security attacks, you need the ability to detect application signatures and monitor your network so that you know what is, and what is not, happening on your network. This allows you to see rogue applications running on your network along with visible footprints that hackers leave as they travel through your systems and networks. The key is to look at a macroscopic, or application view, of the network for IOC.

For instance, suppose there is a foreign actor in Eastern Europe (or other area of the world) that has gained access to your network. Using application data and geo-location information, you would easily be able to see that someone in Eastern Europe is transferring files off of the network from an FTP server in Dallas, Texas back to an address in Eastern Europe. Is this an issue? It depends upon whether you have authorized users in that location or not. If not, it's probably a problem.

Due to application intelligence, you now know that the activity is happening. The rest is up to you to decide if this is an indicator of compromise for your network or not.

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.