Gigamon is launching Hawk, an elastic visibility and analytics fabric for all data-in-motion across any cloud network.
Hawk delivers:
- Elastic visibility for any cloud. Hawk’s visibility-as-code can be embedded into cloud automation to elastically scale-up and scale-out on demand. A single, consumption-based licensing model operates seamlessly across any cloud, public or private.
- Cloud visibility for network tools. Hawk provides traditional network tools with immediate, agentless visibility into layers 2-7 across any cloud network.
- Network visibility for cloud tools. Hawk delivers the “ground truth” of data-in-motion to cloud tools, such as visibility into east-west container traffic and unmanaged devices, through network application metadata.
Thanks to this elastic visibility, Gigamon Hawk radically simplifies hybrid infrastructure, eliminates security and compliance holes and provides IT teams full visibility of their cloud environments at scale. Hawk is available in a subscription, scale-as-you-grow, business model including embedded support and is comprised of:
- A suite of visibility nodes that can scale-up and scale-out as needed across any cloud network
- A cloud data warehouse for security and operational analytic applications
- A single, simple interface for either drag-and-drop manageability or programmatic orchestration
Hawk is integrated with AWS and other leading cloud platforms and tools, providing a unified view across hybrid infrastructure. Hawk for AWS includes features such as elastic visibility that automatically scales out to capture traffic from new EC2 instances, efficient distribution of mirrored traffic to multiple tool destinations, and the ability to extract and store network and application metadata in an AWS storage bucket for near real-time or historical analysis.
“Helping our customers derive value from their cloud investment and solutions is the most important aspect of our work at AWS. As organizations move workloads to the cloud, they want to ensure that they have clear visibility around potential vulnerabilities in their environment. Using AWS with Gigamon Hawk, for example by leveraging Amazon Athena to analyze application metadata collected by Hawk in S3 buckets, customers can gain the visibility they need across their hybrid – or pure cloud – infrastructure to be confident in its security, performance and scalability,” commented Scott Ward, Principal Solutions Architect at AWS.
“We are seeing most of our clients accelerate the movement of their mission-critical apps and workloads to the cloud, resulting in increasingly complex hybrid cloud infrastructures and interactions. This rising complexity challenges IT and InfoSec teams to provide the comprehensive traffic visibility and control that has become the hallmark of optimized and secure networks required to deliver the best user experience. Because traditional network monitoring tools struggle with visibility into cloud activity, increasing cloud adoption will heighten the presence and criticality of network blind spots. Here, cloud visibility and control problems can best be solved by next generation, cloud visibility solutions like those from Gigamon,” said Mark Leary, Research Director with IDC.
“While the path to the cloud varies for every enterprise, managing IT complexity is the universal challenge we have seen among the more than 700 customers who have purchased our cloud visibility fabric. We are proud to be working with AWS and other cloud leaders to deliver solutions that simplify our customers’ ability to reap the benefits of cloud adoption,” said Michael Dickman, Chief Product Officer at Gigamon. “With Hawk, we enable enterprises to simplify and secure today’s hybrid cloud networks and feel confident in their ability to scale for tomorrow’s business needs.”
The Latest
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 ...
In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...
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 ...