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Sumo Logic Global Intelligence Service Announced

Sumo Logic announced a number of new innovations designed to extend machine learning and insights to new teams and use cases across the enterprise.

Specifically:

- Sumo Industry Insights – Sumo Logic’s ‘State of Modern Applications and DevSecOps in the Cloud’ report, now in its third year, provides an unparalleled view into how the world’s most innovative companies are adopting the latest application technologies.

- Sumo Community Insights – Sumo Logic is introducing Sumo Community Insights, which will deliver the industry’s first real-time operational and security insights leveraging machine-learning powered algorithms that uncover global key performance indicators (KPIs) and key risk indicators (KRIs) from the Sumo Logic user community. This will allow organizations to benchmark and compare their own technology decisions and performance against the leading adopters of modern application stacks.This program will inform customers in every part of their development, production and security lifecycles to optimize costs and efficiencies. Example use cases include choosing the right cloud services for their unique needs, benchmarking system and application performance, and analyzing trends and anomalies across peers.

- Sumo Data Science Insights – Sumo Logic is announcing a new open source integration with Jupyter and Apache Zeppelin notebooks that empowers data science teams to build and train machine learning algorithms and models using valuable machine data extracted by the Sumo Logic platform.

Sumo Logic also unveiled a number of new enhancements to help users optimize IT operations, infrastructures and applications with real-time insights, to move from reactive to proactive monitoring and troubleshooting, including:

- Open Platform APIs – new platform APIs will give Sumo Logic customers fine-grained control over the robust role-based access control (RBAC) framework and over the updated content management functionality. This will allow users to automate key configuration tasks like adding new users and permissions.

- Search Templates – empower non-expert users outside of engineering and IT teams such as customer support and product management, to leverage Sumo Logic’s powerful analytics without learning the query language.

- Automatic Rollups for Logs to Metrics – allow users to extract business KPIs from logs at the granularity that makes sense from a cost-to-value perspective. Take millions of customer measurements from logs and cost-effectively convert them to high performance metrics for long-term trending and analysis.

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.

Sumo Logic Global Intelligence Service Announced

Sumo Logic announced a number of new innovations designed to extend machine learning and insights to new teams and use cases across the enterprise.

Specifically:

- Sumo Industry Insights – Sumo Logic’s ‘State of Modern Applications and DevSecOps in the Cloud’ report, now in its third year, provides an unparalleled view into how the world’s most innovative companies are adopting the latest application technologies.

- Sumo Community Insights – Sumo Logic is introducing Sumo Community Insights, which will deliver the industry’s first real-time operational and security insights leveraging machine-learning powered algorithms that uncover global key performance indicators (KPIs) and key risk indicators (KRIs) from the Sumo Logic user community. This will allow organizations to benchmark and compare their own technology decisions and performance against the leading adopters of modern application stacks.This program will inform customers in every part of their development, production and security lifecycles to optimize costs and efficiencies. Example use cases include choosing the right cloud services for their unique needs, benchmarking system and application performance, and analyzing trends and anomalies across peers.

- Sumo Data Science Insights – Sumo Logic is announcing a new open source integration with Jupyter and Apache Zeppelin notebooks that empowers data science teams to build and train machine learning algorithms and models using valuable machine data extracted by the Sumo Logic platform.

Sumo Logic also unveiled a number of new enhancements to help users optimize IT operations, infrastructures and applications with real-time insights, to move from reactive to proactive monitoring and troubleshooting, including:

- Open Platform APIs – new platform APIs will give Sumo Logic customers fine-grained control over the robust role-based access control (RBAC) framework and over the updated content management functionality. This will allow users to automate key configuration tasks like adding new users and permissions.

- Search Templates – empower non-expert users outside of engineering and IT teams such as customer support and product management, to leverage Sumo Logic’s powerful analytics without learning the query language.

- Automatic Rollups for Logs to Metrics – allow users to extract business KPIs from logs at the granularity that makes sense from a cost-to-value perspective. Take millions of customer measurements from logs and cost-effectively convert them to high performance metrics for long-term trending and analysis.

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.