Skip to main content

Gigamon Hawk Adds New Integration with FireEye

Gigamon announced its latest Gigamon Hawk technical integration with FireEye.

In an advancement of their long-standing relationship, the companies worked closely to integrate Gigamon Hawk, the first elastic visibility and analytics fabric for all data-in-motion across the hybrid cloud. Unlike existing visibility tools, only Hawk is built on a single architecture that spans the entire hybrid infrastructure and can elastically scale to provide visibility across any cloud. This enables IT teams to gain full visibility and control of the performance, security and cost of their hybrid cloud network.

The need for organizational agility has driven the rapid evolution of digital infrastructure. In order to optimize and secure these environments, IT teams have implemented additional applications and management tools at a hurried pace, creating a foundational gap in visibility across the underlying hybrid cloud network. Gigamon Hawk is now integrated with FireEye Network Security, radically simplifying hybrid cloud adoption a unified view across hybrid infrastructure through a single, simple interface with built-in management and reporting.

“Gigamon Hawk establishes an important precedent for cloud visibility capabilities,” said Ramesh Gupta, SVP, Engineering for Network Security at FireEye. “It is not enough to maintain legacy monitoring and security tools, especially as the hybrid workforce remains, and depends, on the cloud to continue business operations as normal. Gigamon Hawk will enable our customers to improve their security posture and gain unified insight into their hybrid cloud.”

“Gigamon has long been at the forefront of network visibility, now delivering a hybrid cloud platform that comes to life via trusted partners like FireEye,” said Michael Dickman, Chief Product Officer at Gigamon. “With Gigamon Hawk customers gain the ability to deploy FireEye cloud network security solutions instantly, and automatically scale out traffic visibility using platform-native automation offerings. Hawk allows IT to create a cloud ‘landing zone’ that includes FireEye and other critical security controls, with the option to refactor for cloud-native security controls, compliance and policies. We are proud to partner with FireEye to simplify, secure and optimize hybrid cloud environments.”

The Latest

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

Gigamon Hawk Adds New Integration with FireEye

Gigamon announced its latest Gigamon Hawk technical integration with FireEye.

In an advancement of their long-standing relationship, the companies worked closely to integrate Gigamon Hawk, the first elastic visibility and analytics fabric for all data-in-motion across the hybrid cloud. Unlike existing visibility tools, only Hawk is built on a single architecture that spans the entire hybrid infrastructure and can elastically scale to provide visibility across any cloud. This enables IT teams to gain full visibility and control of the performance, security and cost of their hybrid cloud network.

The need for organizational agility has driven the rapid evolution of digital infrastructure. In order to optimize and secure these environments, IT teams have implemented additional applications and management tools at a hurried pace, creating a foundational gap in visibility across the underlying hybrid cloud network. Gigamon Hawk is now integrated with FireEye Network Security, radically simplifying hybrid cloud adoption a unified view across hybrid infrastructure through a single, simple interface with built-in management and reporting.

“Gigamon Hawk establishes an important precedent for cloud visibility capabilities,” said Ramesh Gupta, SVP, Engineering for Network Security at FireEye. “It is not enough to maintain legacy monitoring and security tools, especially as the hybrid workforce remains, and depends, on the cloud to continue business operations as normal. Gigamon Hawk will enable our customers to improve their security posture and gain unified insight into their hybrid cloud.”

“Gigamon has long been at the forefront of network visibility, now delivering a hybrid cloud platform that comes to life via trusted partners like FireEye,” said Michael Dickman, Chief Product Officer at Gigamon. “With Gigamon Hawk customers gain the ability to deploy FireEye cloud network security solutions instantly, and automatically scale out traffic visibility using platform-native automation offerings. Hawk allows IT to create a cloud ‘landing zone’ that includes FireEye and other critical security controls, with the option to refactor for cloud-native security controls, compliance and policies. We are proud to partner with FireEye to simplify, secure and optimize hybrid cloud environments.”

The Latest

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...