Skip to main content

Sumo Logic Adds New New Observability, Security and AI Features

Sumo Logic announced a number of new innovations and updates that help users accelerate troubleshooting and security across AWS environments, within a span of minutes and a few clicks.

Sumo Logic's new solutions and features purpose-built for AWS help users find the root cause of performance, availability, and security issues faster than ever including:

- Sumo Logic Log Analytics for AWS - this is a new packaged solution that delivers a curated view and a single pane of glass for monitoring and troubleshooting AWS services easily and effectively. The zero configuration solution automatically collects logs and metrics data from 12 core AWS services including EC2, Lambda, ECS, RDS, DynamoDB, API GW, and Load Balancers, in one single step. Users can now get full visibility across different AWS accounts and regions, and leverage ML-powered analytics to troubleshoot at lightning speed, with significantly lower time to value, as the solution can be deployed in minutes. In addition, organizations control costs by optimizing AWS-spend across the environment and help users better understand where they are at with application and infrastructure performance globally across AI-powered Global Intelligence benchmarks.

- CIS for AWS - Sumo Logic's new Cloud Infrastructure Security (CIS) for AWS provides an enterprise-wide, unified view of your AWS infrastructure that delivers insights into active threats, non-compliant security controls and suspicious activity across complex AWS environments - spanning multiple accounts, users, regions, and resource types. The solution delivers a curated workflow purpose-built for AWS. And an enterprise-wide unified view of your AWS infrastructure delivers insights into active threats, non-compliant security controls and suspicious activity across complex AWS environments spanning multiple accounts, users, regions and resource types.

- AI-Driven Alerting - This new feature enables users to harness the power of advanced anomaly detection, machine learning and intelligent playbooks, in order to reduce the noise of daily alerts and false alarms by highlighting the most critical issues that require immediate attention. The solution can also be used in conjunction with playbooks to automate incident resolution actions swiftly, such as server restarts and capacity provisioning.

- Global Intelligence for AWS CloudTrail DevOps - Sumo Logic’s AI-powered application is designed to help DevOps professionals deliver deep insights into AWS performance and configuration. In addition, users can leverage for quick issue detection and resolution, alongside machine learning models derived from extensive data gathered from Sumo Logic's AWS customer logs for troubleshooting.

- Global Intelligence for AWS CloudTrail SecOps - This application enables SecOps professionals to proactively detect potentially malicious configuration changes in your AWS account by using a machine learning model to compare AWS CloudTrail events against a cohort of AWS customers. These CloudTrail events are carefully curated from AWS penetration tests and operational best practices.

“Every organization across every industry is transforming, relying on digital and cloud to accelerate innovation, develop a competitive edge and help service their customers better. But these initiatives often lead to significant complexities for operations and security teams,” said Joe Kim, President and CEO, for Sumo Logic. “We believe logs are the fundamental source of truth that brings Dev, Sec, and Ops together, and we’re excited about these new innovations and updates we’re showcasing this week to further strengthen our cloud-native SaaS Log Analytics Platform to provide a single, unified view, that allows users to go from insights to action, fast.”

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 Adds New New Observability, Security and AI Features

Sumo Logic announced a number of new innovations and updates that help users accelerate troubleshooting and security across AWS environments, within a span of minutes and a few clicks.

Sumo Logic's new solutions and features purpose-built for AWS help users find the root cause of performance, availability, and security issues faster than ever including:

- Sumo Logic Log Analytics for AWS - this is a new packaged solution that delivers a curated view and a single pane of glass for monitoring and troubleshooting AWS services easily and effectively. The zero configuration solution automatically collects logs and metrics data from 12 core AWS services including EC2, Lambda, ECS, RDS, DynamoDB, API GW, and Load Balancers, in one single step. Users can now get full visibility across different AWS accounts and regions, and leverage ML-powered analytics to troubleshoot at lightning speed, with significantly lower time to value, as the solution can be deployed in minutes. In addition, organizations control costs by optimizing AWS-spend across the environment and help users better understand where they are at with application and infrastructure performance globally across AI-powered Global Intelligence benchmarks.

- CIS for AWS - Sumo Logic's new Cloud Infrastructure Security (CIS) for AWS provides an enterprise-wide, unified view of your AWS infrastructure that delivers insights into active threats, non-compliant security controls and suspicious activity across complex AWS environments - spanning multiple accounts, users, regions, and resource types. The solution delivers a curated workflow purpose-built for AWS. And an enterprise-wide unified view of your AWS infrastructure delivers insights into active threats, non-compliant security controls and suspicious activity across complex AWS environments spanning multiple accounts, users, regions and resource types.

- AI-Driven Alerting - This new feature enables users to harness the power of advanced anomaly detection, machine learning and intelligent playbooks, in order to reduce the noise of daily alerts and false alarms by highlighting the most critical issues that require immediate attention. The solution can also be used in conjunction with playbooks to automate incident resolution actions swiftly, such as server restarts and capacity provisioning.

- Global Intelligence for AWS CloudTrail DevOps - Sumo Logic’s AI-powered application is designed to help DevOps professionals deliver deep insights into AWS performance and configuration. In addition, users can leverage for quick issue detection and resolution, alongside machine learning models derived from extensive data gathered from Sumo Logic's AWS customer logs for troubleshooting.

- Global Intelligence for AWS CloudTrail SecOps - This application enables SecOps professionals to proactively detect potentially malicious configuration changes in your AWS account by using a machine learning model to compare AWS CloudTrail events against a cohort of AWS customers. These CloudTrail events are carefully curated from AWS penetration tests and operational best practices.

“Every organization across every industry is transforming, relying on digital and cloud to accelerate innovation, develop a competitive edge and help service their customers better. But these initiatives often lead to significant complexities for operations and security teams,” said Joe Kim, President and CEO, for Sumo Logic. “We believe logs are the fundamental source of truth that brings Dev, Sec, and Ops together, and we’re excited about these new innovations and updates we’re showcasing this week to further strengthen our cloud-native SaaS Log Analytics Platform to provide a single, unified view, that allows users to go from insights to action, fast.”

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.