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Gigamon Announces GigaVUE Cloud Suite with Amazon Virtual Private Cloud Traffic Mirroring Service

Gigamon announced that GigaVUE Cloud Suite for Amazon Web Services (AWS) now features native support for Amazon Virtual Private Cloud (Amazon VPC) traffic mirroring.

This service makes agentless-based traffic acquisition easier to obtain and more effective, coming at a crucial time as organizations dramatically expand the number and complexity of applications they deploy with AWS.

As organizations relocate or develop new workloads in the cloud, these applications challenge IT teams to provide the required security, performance and customer experience. Traffic intelligence between, and within, these distributed mission critical applications is key to the success of the modern digital enterprise as they strive to run fast and stay secure.

GigaVUE Cloud Suite with Amazon VPC traffic mirroring is the most effective approach for network-based security and performance analysis. Through threat and behavior analysis, the enhanced approach expedites detection of targeted attacks, insider abuse and compromised workloads.

Gigamon support for the Amazon VPC traffic mirroring service allows customers to gain full access to, and insight into, the network traffic across their infrastructure for content inspection and threat monitoring. With this service, organizations can copy all network packets at any Elastic Network Interface (ENI) in their Amazon VPCs and send them to the GigaVUE Cloud Suite for AWS for traffic aggregation, advanced processing and proper distribution to the appropriate security and monitoring tools.

With the Gigamon V Series, which provides traffic intelligence in public cloud environments, AWS customers can eliminate unnecessary load on these tools by offloading processor-intensive tasks such as slicing and masking. GigaVUE Cloud Suite for AWS also generates NetFlow to help provide context to this traffic and then directs this modified traffic, via Flow Mapping, to the appropriate tool.

In addition to utilizing Amazon VPC traffic mirroring, Gigamon further announces advanced support for AWS with Fabric Manager (FM) centralized management and orchestration solution. FM can now:

- Detect Amazon Elastic Compute Cloud (Amazon EC2) changes in an Amazon VPC, and automatically control the Gigamon V Series node visibility tier using AWS APIs

- Eliminate manual processes and errors by automatically identifying each new workload and their associated traffic mirroring, and then configuring the traffic mirroring service to direct traffic to the V Series Nodes

- Integrate with third-party systems and tools, via RESTful APIs, to dynamically and automatically direct received traffic and configure new traffic policies

- Auto discover and visualize the end-to-end topology of visibility tiers and Amazon EC2 instances

The Latest

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.

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 ...

Gigamon Announces GigaVUE Cloud Suite with Amazon Virtual Private Cloud Traffic Mirroring Service

Gigamon announced that GigaVUE Cloud Suite for Amazon Web Services (AWS) now features native support for Amazon Virtual Private Cloud (Amazon VPC) traffic mirroring.

This service makes agentless-based traffic acquisition easier to obtain and more effective, coming at a crucial time as organizations dramatically expand the number and complexity of applications they deploy with AWS.

As organizations relocate or develop new workloads in the cloud, these applications challenge IT teams to provide the required security, performance and customer experience. Traffic intelligence between, and within, these distributed mission critical applications is key to the success of the modern digital enterprise as they strive to run fast and stay secure.

GigaVUE Cloud Suite with Amazon VPC traffic mirroring is the most effective approach for network-based security and performance analysis. Through threat and behavior analysis, the enhanced approach expedites detection of targeted attacks, insider abuse and compromised workloads.

Gigamon support for the Amazon VPC traffic mirroring service allows customers to gain full access to, and insight into, the network traffic across their infrastructure for content inspection and threat monitoring. With this service, organizations can copy all network packets at any Elastic Network Interface (ENI) in their Amazon VPCs and send them to the GigaVUE Cloud Suite for AWS for traffic aggregation, advanced processing and proper distribution to the appropriate security and monitoring tools.

With the Gigamon V Series, which provides traffic intelligence in public cloud environments, AWS customers can eliminate unnecessary load on these tools by offloading processor-intensive tasks such as slicing and masking. GigaVUE Cloud Suite for AWS also generates NetFlow to help provide context to this traffic and then directs this modified traffic, via Flow Mapping, to the appropriate tool.

In addition to utilizing Amazon VPC traffic mirroring, Gigamon further announces advanced support for AWS with Fabric Manager (FM) centralized management and orchestration solution. FM can now:

- Detect Amazon Elastic Compute Cloud (Amazon EC2) changes in an Amazon VPC, and automatically control the Gigamon V Series node visibility tier using AWS APIs

- Eliminate manual processes and errors by automatically identifying each new workload and their associated traffic mirroring, and then configuring the traffic mirroring service to direct traffic to the V Series Nodes

- Integrate with third-party systems and tools, via RESTful APIs, to dynamically and automatically direct received traffic and configure new traffic policies

- Auto discover and visualize the end-to-end topology of visibility tiers and Amazon EC2 instances

The Latest

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.

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 ...