
Sumo Logic acquired FactorChain, an early stage security company with a Security Investigation Platform that makes transformational improvements in speed and depth of threat investigations, enabling resolution of complex threats spanning both IT and security in minutes instead of hours.
Combined with Sumo Logic’s machine data analytics strength across the cloud infrastructure, platform and application layers, this acquisition accelerates the development of a new class of converged IT ops and security solutions, which are essential for modern application delivery in the cloud.
“We are seeing more and more digital businesses struggling to adapt traditional security models to cloud and modern application delivery,” said Ramin Sayar, CEO of Sumo Logic. “Resolving which threats to assess in the avalanche of alerts and how to quickly resolve them is a challenging enough problem in traditional IT environments. However, in the dynamic large-scale cloud environment, this problem can’t possibly be answered without new thinking in security analytics and operations. The FactorChain acquisition accelerates our vision of a new model that converges operations and security in the cloud and provides innovative solutions to persistent challenges in investigation workflows at cloud scale. We’re thrilled to welcome the team onboard to drive our shared vision and disrupt traditional legacy SIEM markets moving too slowly to keep pace with accelerating cloud transitions.”
As part of Sumo Logic’s security analytics solution, FactorChain’s investigation platform will integrate completely into Sumo Logic’s SaaS Machine Data Analytics Platform, providing step function improvements in speed, accuracy, and retained learning of threat investigation workflows across IT and security. Integrated data, analytics and workflow will enable analysts to resolve complex investigations in minutes, while quickly identifying infection spread and applying accumulated learning across IT and security teams. Leveraging the FactorChain acquired domain expertise, natively developed capabilities to correlate and prioritize threats with unique insight across the application and infrastructure layer will focus joint security and IT teams on the right threats to investigate. Learning on both workflow and data models from these services will provide insight on other IT and security processes including alert tuning, configuration, policy, prioritization, etc.
“Cloud and modern application deployments demand a fundamentally new approach to security threat investigation – workflows must span both the application and infrastructure layers, integrate seamlessly across both security and IT ops, and enable resolution in minutes,” said Dave Frampton, founder and CEO of FactorChain. “FactorChain’s innovations in web speed contextual search, information models for rapid insight in large data sets, and investigation workflows which maximize human and machine synergy will combine with Sumo Logic’s machine data analytics platform to scale security for cloud and modern application delivery. We’re thrilled to join the Sumo Logic team and pursue our shared vision of a new converged security IT model for the cloud.”
As part of the acquisition, Sumo Logic has incorporated the entire FactorChain team along with industry veteran leadership into its engineering and product teams. Dave Frampton, FactorChain co-founder and CEO, will be VP of security solutions leading the company’s security analytics business. Ken Tidwell, FactorChain co-founder and CTO and former chief architect of ArcSight, will be VP of security engineering leading the security analytics development and architecture.
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.