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Corvil Launches New Software-Defined Sensor

Corvil announced Corvil Sensor, a software-defined solution for packet-level instrumentation of virtual machines in public, private and hybrid cloud infrastructures.

Corvil Sensor uses a Smart Streaming architecture optimized for real-time, reliable and always-on monitoring and analysis of business-critical workloads in the Cloud.

With Corvil Sensor, customers can now extend the comprehensive operational, security and business analytics solution provided by Corvil to public, private and hybrid cloud architectures.

Corvil sensor is provided as a low-overhead software daemon that can be instantiated on a virtual machine in seconds. Full Corvil analytics can be instrumented dynamically and broadly within the Cloud with live insights and intelligence for HTTP, Database and Storage and other applications displayed on existing customer dashboards within a few minutes of initial turn-up of the Corvil Sensor. The solution assures seamless and reliable access to intelligence from time-stamped packet data streams across private and public infrastructure, giving Corvil customers the same level of diagnostics, forensics and analytics they use to operate their business in non-Cloud infrastructure with no new tooling or training. Corvil Sensor also allows customers to instantly compare performance and application behavior between on-prem and cloud-deployed workloads, and between different cloud providers, side-by-side on the same dashboards.

“Corvil Sensor is a game changer for customers wishing to leverage our real-time analytics and forensics solution in the cloud,” says Donal O’Sullivan, VP of Product Management at Corvil. “We believe our unique approach for assuring reliable and smart delivery of streaming packet data from connected virtual machines achieves superior performance and lower cost compared to competing approaches.”

Corvil Sensor’s software instrumentation empowers organizations to:

- Maintain the same packet-level visibility before, during and after workload migration to public, private or hybrid cloud architectures.

- Obtain complete understanding of how software, network, load-balancers, firewalls and other cloud infrastructure impact application performance.

- Achieve enterprise grade network forensics for advanced cybersecurity surveillance.

- Simplify and automate deployment of ubiquitous monitoring coverage to improve productivity.

- Dramatically reduce the cost of extending packet-level visibility and analytics to cloud and remote infrastructures that were previously cost prohibitive

With Corvil Sensor, early access customers have already seen the following benefits:

- A global bank eliminated the need for physical appliance deployment in its private cloud infrastructure – reducing application deployment time from weeks to minutes.

- A large US bank eliminated 4 days of troubleshooting time spent obtaining packets from its cloud infrastructure provider.

- A SaaS company protected workload migration from on-premise to public cloud infrastructure by using packet-based surveillance to eliminate security blind-spots.

Corvil Sensor will be generally available starting in May 2017 and is offered for free to all customers.

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Corvil Launches New Software-Defined Sensor

Corvil announced Corvil Sensor, a software-defined solution for packet-level instrumentation of virtual machines in public, private and hybrid cloud infrastructures.

Corvil Sensor uses a Smart Streaming architecture optimized for real-time, reliable and always-on monitoring and analysis of business-critical workloads in the Cloud.

With Corvil Sensor, customers can now extend the comprehensive operational, security and business analytics solution provided by Corvil to public, private and hybrid cloud architectures.

Corvil sensor is provided as a low-overhead software daemon that can be instantiated on a virtual machine in seconds. Full Corvil analytics can be instrumented dynamically and broadly within the Cloud with live insights and intelligence for HTTP, Database and Storage and other applications displayed on existing customer dashboards within a few minutes of initial turn-up of the Corvil Sensor. The solution assures seamless and reliable access to intelligence from time-stamped packet data streams across private and public infrastructure, giving Corvil customers the same level of diagnostics, forensics and analytics they use to operate their business in non-Cloud infrastructure with no new tooling or training. Corvil Sensor also allows customers to instantly compare performance and application behavior between on-prem and cloud-deployed workloads, and between different cloud providers, side-by-side on the same dashboards.

“Corvil Sensor is a game changer for customers wishing to leverage our real-time analytics and forensics solution in the cloud,” says Donal O’Sullivan, VP of Product Management at Corvil. “We believe our unique approach for assuring reliable and smart delivery of streaming packet data from connected virtual machines achieves superior performance and lower cost compared to competing approaches.”

Corvil Sensor’s software instrumentation empowers organizations to:

- Maintain the same packet-level visibility before, during and after workload migration to public, private or hybrid cloud architectures.

- Obtain complete understanding of how software, network, load-balancers, firewalls and other cloud infrastructure impact application performance.

- Achieve enterprise grade network forensics for advanced cybersecurity surveillance.

- Simplify and automate deployment of ubiquitous monitoring coverage to improve productivity.

- Dramatically reduce the cost of extending packet-level visibility and analytics to cloud and remote infrastructures that were previously cost prohibitive

With Corvil Sensor, early access customers have already seen the following benefits:

- A global bank eliminated the need for physical appliance deployment in its private cloud infrastructure – reducing application deployment time from weeks to minutes.

- A large US bank eliminated 4 days of troubleshooting time spent obtaining packets from its cloud infrastructure provider.

- A SaaS company protected workload migration from on-premise to public cloud infrastructure by using packet-based surveillance to eliminate security blind-spots.

Corvil Sensor will be generally available starting in May 2017 and is offered for free to all customers.

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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.