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Continuity Launches AvailabilityGuard 7

Continuity Software announced the release of version 7 of AvailabilityGuard software, providing enterprise IT teams with advanced predictive analytics, risk detection, and outage prevention capabilities across their software-defined datacenters.

AvailabilityGuard provides infrastructure teams with predictive IT Operations Analytics capabilities that ensure resiliency, high availability, and operational excellence in the highly dynamic environment of the software-defined datacenter.

AvailabilityGuard allows IT organizations to proactively identify and mitigate hidden design and deployment flaws that may introduce downtime risks, single-points-of-failure, and deviations from best practices across the entire infrastructure.

“The transition towards the software-defined datacenter delivers higher levels of agility and control to IT organizations, but at the same time presents new challenges,” said Doron Pinhas, CTO, Continuity Software. “The risk of misconfiguration does not go away with automation. If a certain configuration deviates from best practices, automation only helps it spread faster and makes it more difficult to pinpoint. AvailabilityGuard is a safeguard against the diffusion of bad practices and risky configurations throughout the infrastructure.”

AvailabilityGuard enables IT teams to ensure that all systems of their respective Software Defined Data Centers are properly configured according to vendor best practices and internally-defined standards across all virtual and physical layers by:

- Performing daily verification of the entire IT landscape to identify single-points-of-failure and other configuration risks while reducing time and effort associated with pre-rollout testing

- Verifying configuration changes before they impact the business

- Providing actionable recommendations for applying best practices and removing availability risks

- Measuring KPIs that support continuous improvement to establish safer and more agile best practices over time.

Additionally, AvailabilityGuard helps IT organizations realize the benefits of the Software-Defined Datacenter with greater confidence, providing a blueprint for safer transition towards automation by verifying the existing environment to ensure a clean start, validating that automation scripts are programmed correctly, and ensuring ongoing automated validation following the transition.

“Automating standard validation means you get things done right from day one, as opposed to learning how to get there after years of trial-and-error,” added Pinhas. “Best practices not only protect against risks to your environment, but also help you get the best utilization and performance from your technology and make systems easier to maintain and update.”

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Continuity Launches AvailabilityGuard 7

Continuity Software announced the release of version 7 of AvailabilityGuard software, providing enterprise IT teams with advanced predictive analytics, risk detection, and outage prevention capabilities across their software-defined datacenters.

AvailabilityGuard provides infrastructure teams with predictive IT Operations Analytics capabilities that ensure resiliency, high availability, and operational excellence in the highly dynamic environment of the software-defined datacenter.

AvailabilityGuard allows IT organizations to proactively identify and mitigate hidden design and deployment flaws that may introduce downtime risks, single-points-of-failure, and deviations from best practices across the entire infrastructure.

“The transition towards the software-defined datacenter delivers higher levels of agility and control to IT organizations, but at the same time presents new challenges,” said Doron Pinhas, CTO, Continuity Software. “The risk of misconfiguration does not go away with automation. If a certain configuration deviates from best practices, automation only helps it spread faster and makes it more difficult to pinpoint. AvailabilityGuard is a safeguard against the diffusion of bad practices and risky configurations throughout the infrastructure.”

AvailabilityGuard enables IT teams to ensure that all systems of their respective Software Defined Data Centers are properly configured according to vendor best practices and internally-defined standards across all virtual and physical layers by:

- Performing daily verification of the entire IT landscape to identify single-points-of-failure and other configuration risks while reducing time and effort associated with pre-rollout testing

- Verifying configuration changes before they impact the business

- Providing actionable recommendations for applying best practices and removing availability risks

- Measuring KPIs that support continuous improvement to establish safer and more agile best practices over time.

Additionally, AvailabilityGuard helps IT organizations realize the benefits of the Software-Defined Datacenter with greater confidence, providing a blueprint for safer transition towards automation by verifying the existing environment to ensure a clean start, validating that automation scripts are programmed correctly, and ensuring ongoing automated validation following the transition.

“Automating standard validation means you get things done right from day one, as opposed to learning how to get there after years of trial-and-error,” added Pinhas. “Best practices not only protect against risks to your environment, but also help you get the best utilization and performance from your technology and make systems easier to maintain and update.”

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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.