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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.”

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For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

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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

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...