
Elastic is accelerating the adoption of AI-driven security analytics by automating SIEM data onboarding with Automatic Import. This new feature — the only one of its kind for a security analytics or SIEM solution — automates the development of custom data integrations.
With Automatic Import, Elastic Security now adds custom data sources faster than any competing security analytics solution— from up to several days to less than ten minutes— facilitating broader visibility and easier SIEM implementation.
One of Elastic’s largest security customers recently migrated nearly 200 data sources, including many custom technologies. Customers of this scale can now save hundreds of hours of consulting time and weeks to months of implementation time. “Automatic Import addresses one of the biggest headaches of switching SIEMs: onboarding custom data sources,” said Michelle Abraham, research director, Security and Trust at IDC.
“The feature automates the development of new data integrations, reducing the cost, complexity and stress of migration.” Automatic Import applies generative AI to expedite labor-intensive SecOps tasks, building on previous Elastic AI-driven security analytics innovations like Elastic AI Assistant, which answers security questions and guides practitioner workflows, and Attack Discovery, which automates alert triage.
Elastic can mitigate the security challenges intrinsic to fast-changing environments and messy data due to the company’s ability to handle unstructured data at scale and surface relevant insights via Large Language Models and RAG. Powered by the Elastic Search AI Platform, Automatic Import provides model-agnostic access to harness the knowledge from large language models (LLMs) and the ability to ground answers in proprietary data using retrieval augmented generation (RAG). This is made possible by the flexibility of Search AI Lake and the company’s expertise in enabling security teams to leverage data of any kind.
“Automatic Import makes building and testing custom data integrations easier, helping us quickly enhance visibility throughout our environment,” said Nate Thompson, senior manager, Cybersecurity Analytics & Automation at Dana Inc. Elastic Security ships with 400+ prebuilt data integrations, and Automatic Import makes it practical to extend visibility beyond these to an evolving array of security-relevant technologies and applications. These integrations normalize data to Elastic Common Schema (ECS), enabling uniform analysis with dashboards, search, alerting, machine learning, and more.
Establishing visibility across an enterprise IT environment is inherently difficult, but no matter how the attack surface changes, security teams can’t afford to fly blind. Until now, onboarding custom data has been costly and complex,” said Mike Nichols, vice president of product for security at Elastic. “Automatic Import arrives at a critical moment to address these challenges, as organizations explore replacement options for their legacy SIEM tools.”
Automatic Import is launching with support for Anthropic models via Elastic’s connector for Amazon Bedrock. It supports JSON and NDJSON-based log formats. Automatic Import is available to all customers with an Enterprise license.
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.
