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Gigamon Releases GigaVUE 6.0

Gigamon announced an expansive set of new capabilities to the Gigamon Hawk Deep Observability Pipeline.

GigaVUE 6.0, the software powering Gigamon Hawk, democratizes security delivery across networking, security, and cloud operations teams while dramatically reducing cross-platform cost and complexity.

Gigamon Hawk helps IT teams scale up and scale out with confidence, eliminate security and performance blind spots across cloud traffic and unmanaged hosts, and gain application transparency to secure against threats-in-motion across distributed hybrid and multi-cloud infrastructure. Gigamon Hawk amplifies the power of observability by accessing network traffic at the source, whether virtual, container, or physical, and efficiently aggregates, optimizes, and enriches it while serving actionable, network-level intelligence to any tooling, anywhere.

Today’s release introduces a series of major advancements including:

- Next-Gen Container Network Visibility – GigaVUE now enables the acquisition of container traffic, delivering the industry’s first universal container tap (UCT) solution deployable with any container network interface (CNI) and any container orchestration, powered by the enhanced Berkeley Packet Filter (eBPF) for network-level observability with minimal overhead. Deep observability for security and performance can now follow the workload no matter the ephemerality or scale of container-based micro-services.

- Application Metadata Integration Framework – Gigamon Hawk application metadata engine integrates directly with leading observability platforms, including Dynatrace, New Relic, and Sumo Logic, using JavaScript object notation (JSON) and Kafka. This integration enables existing observability tools to perform new security functions, such as identifying rogue services, activities, and illegal crypto mining.

- Cloud-Scale Network Telemetry Processing – The new GigaVUE-HC1 Plus visibility appliance, an underlying component of Gigamon Hawk, delivers twice the pipeline processing performance in half the physical footprint and power requirements, compared to the previous generation. This purpose-built appliance can efficiently and economically process network telemetry by aggregating telemetry from physical, virtual, and/or container workloads, including telemetry from public cloud workloads.

“Today’s GigaVUE 6.0 software release provides IT organizations with next-generation, actionable network-level intelligence that delivers granularity up to the application layer for all workloads including private and public cloud and all container environments,” said Michael Dickman, CPO at Gigamon. “Our deep observability pipeline is the realization of our vision to bring rich, ‘ground truth’ network intelligence to our customers, allowing them to solve their most pressing security and operational challenges to manage distributed hybrid and multi-cloud infrastructure efficiently and effectively.”

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Gigamon Releases GigaVUE 6.0

Gigamon announced an expansive set of new capabilities to the Gigamon Hawk Deep Observability Pipeline.

GigaVUE 6.0, the software powering Gigamon Hawk, democratizes security delivery across networking, security, and cloud operations teams while dramatically reducing cross-platform cost and complexity.

Gigamon Hawk helps IT teams scale up and scale out with confidence, eliminate security and performance blind spots across cloud traffic and unmanaged hosts, and gain application transparency to secure against threats-in-motion across distributed hybrid and multi-cloud infrastructure. Gigamon Hawk amplifies the power of observability by accessing network traffic at the source, whether virtual, container, or physical, and efficiently aggregates, optimizes, and enriches it while serving actionable, network-level intelligence to any tooling, anywhere.

Today’s release introduces a series of major advancements including:

- Next-Gen Container Network Visibility – GigaVUE now enables the acquisition of container traffic, delivering the industry’s first universal container tap (UCT) solution deployable with any container network interface (CNI) and any container orchestration, powered by the enhanced Berkeley Packet Filter (eBPF) for network-level observability with minimal overhead. Deep observability for security and performance can now follow the workload no matter the ephemerality or scale of container-based micro-services.

- Application Metadata Integration Framework – Gigamon Hawk application metadata engine integrates directly with leading observability platforms, including Dynatrace, New Relic, and Sumo Logic, using JavaScript object notation (JSON) and Kafka. This integration enables existing observability tools to perform new security functions, such as identifying rogue services, activities, and illegal crypto mining.

- Cloud-Scale Network Telemetry Processing – The new GigaVUE-HC1 Plus visibility appliance, an underlying component of Gigamon Hawk, delivers twice the pipeline processing performance in half the physical footprint and power requirements, compared to the previous generation. This purpose-built appliance can efficiently and economically process network telemetry by aggregating telemetry from physical, virtual, and/or container workloads, including telemetry from public cloud workloads.

“Today’s GigaVUE 6.0 software release provides IT organizations with next-generation, actionable network-level intelligence that delivers granularity up to the application layer for all workloads including private and public cloud and all container environments,” said Michael Dickman, CPO at Gigamon. “Our deep observability pipeline is the realization of our vision to bring rich, ‘ground truth’ network intelligence to our customers, allowing them to solve their most pressing security and operational challenges to manage distributed hybrid and multi-cloud infrastructure efficiently and effectively.”

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...