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LogRhythm Integrates with Gigamon Hawk Deep Observability Pipeline

LogRhythm announced a technology integration with Gigamon.

LogRhythm and Gigamon are working together to help organizations around the globe increase visibility and protect against modern cyberattacks. By understanding the power and necessity of visibility, Gigamon and LogRhythm have integrated their solutions — the Gigamon Hawk Deep Observability Pipeline and LogRhythm SIEM Platform. The combined solution empowers security teams to identify behavioral anomalies, internal and external threats, and to prioritize their responses based on accurate enterprise security intelligence.

Threat actors continue to find ways around prevention technology to access an organization’s network and proprietary information. When considering the high amount of network traffic security operators need to sift through each day, it becomes more difficult to survey and analyze the network to detect anomalous behavior. LogRhythm’s integration with Gigamon helps address these challenges by providing organizations with network-derived intelligence and insights needed to proactively detect and respond to threats.

“Our integration with Gigamon allows us to provide customers with visibility across physical, virtual and cloud networks,” said Andrew Hollister, Chief Information Security Officer at LogRhythm. “Security teams will gain the necessary insights to accelerate detection and response to emergent threats, including custom malware and nation-state espionage, as well as routine network misuse and many other types of anomalous behavior.”

Together, the Gigamon Hawk and LogRhythm SIEM Platform integration delivers the awareness needed to detect, prioritize, and neutralize damaging cyber threats that have either penetrated the network perimeter or originated from within. Key benefits of this integration include:

- Actionable network-derived intelligence and easy access to traffic from physical, virtual and cloud networks with the Gigamon Hawk Deep Observability Pipeline.

- Aggregation, filtering, and distribution of relevant traffic to LogRhythm SIEM accelerates processing throughput.

- Masking of private and sensitive data to meet industry regulations before sending to LogRhythm SIEM.

- Generated metadata can be selected from over 7,000 attributes across over 3,000 applications – for example, HTTP response codes and DNS queries – to provide highly detailed contextual analysis when looking at network events.

- Ability to generate NetFlow from any traffic flow and decrypt SSL traffic to avoid unnecessary processing.

- Automatic traffic load balancing helps optimize the performance of LogRhythm SIEM.

“No matter what prevention technology organizations deploy, persistent hackers will find a way in. Therefore, today’s security efforts must focus on proactively detecting and neutralizing malicious activity faster, more effectively, and before severe damage can compromise an entire organization,” said Michael Dickman, chief product officer at Gigamon. “The combined benefits of Gigamon Hawk and LogRhythm SIEM are exactly what organizations need to ensure they can patrol their entire network as it provides network and endpoint monitoring for end-to-end threat lifecycle management.”

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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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LogRhythm Integrates with Gigamon Hawk Deep Observability Pipeline

LogRhythm announced a technology integration with Gigamon.

LogRhythm and Gigamon are working together to help organizations around the globe increase visibility and protect against modern cyberattacks. By understanding the power and necessity of visibility, Gigamon and LogRhythm have integrated their solutions — the Gigamon Hawk Deep Observability Pipeline and LogRhythm SIEM Platform. The combined solution empowers security teams to identify behavioral anomalies, internal and external threats, and to prioritize their responses based on accurate enterprise security intelligence.

Threat actors continue to find ways around prevention technology to access an organization’s network and proprietary information. When considering the high amount of network traffic security operators need to sift through each day, it becomes more difficult to survey and analyze the network to detect anomalous behavior. LogRhythm’s integration with Gigamon helps address these challenges by providing organizations with network-derived intelligence and insights needed to proactively detect and respond to threats.

“Our integration with Gigamon allows us to provide customers with visibility across physical, virtual and cloud networks,” said Andrew Hollister, Chief Information Security Officer at LogRhythm. “Security teams will gain the necessary insights to accelerate detection and response to emergent threats, including custom malware and nation-state espionage, as well as routine network misuse and many other types of anomalous behavior.”

Together, the Gigamon Hawk and LogRhythm SIEM Platform integration delivers the awareness needed to detect, prioritize, and neutralize damaging cyber threats that have either penetrated the network perimeter or originated from within. Key benefits of this integration include:

- Actionable network-derived intelligence and easy access to traffic from physical, virtual and cloud networks with the Gigamon Hawk Deep Observability Pipeline.

- Aggregation, filtering, and distribution of relevant traffic to LogRhythm SIEM accelerates processing throughput.

- Masking of private and sensitive data to meet industry regulations before sending to LogRhythm SIEM.

- Generated metadata can be selected from over 7,000 attributes across over 3,000 applications – for example, HTTP response codes and DNS queries – to provide highly detailed contextual analysis when looking at network events.

- Ability to generate NetFlow from any traffic flow and decrypt SSL traffic to avoid unnecessary processing.

- Automatic traffic load balancing helps optimize the performance of LogRhythm SIEM.

“No matter what prevention technology organizations deploy, persistent hackers will find a way in. Therefore, today’s security efforts must focus on proactively detecting and neutralizing malicious activity faster, more effectively, and before severe damage can compromise an entire organization,” said Michael Dickman, chief product officer at Gigamon. “The combined benefits of Gigamon Hawk and LogRhythm SIEM are exactly what organizations need to ensure they can patrol their entire network as it provides network and endpoint monitoring for end-to-end threat lifecycle management.”

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.