Elastic announced Search AI will replace the traditional SIEM with an AI-driven security analytics solution for the modern SOC.
Powered by the Search AI platform, Elastic Security is replacing largely manual processes for configuration, investigation and response by combining search and retrieval augmented generation (RAG) to provide hyper-relevant results that matter. The newest feature, Attack Discovery, triages 100s of alerts down to the few attacks that matter with a single button click, and returns results in an intuitive interface, allowing security operations teams to quickly understand the most impactful attacks, take immediate follow-up actions and more.
Elastic’s AI-driven security analytics is built on the Search AI platform, which includes RAG powered by the industry's foremost search technology. LLMs are only as accurate and current as the information they leverage: their underlying training data and the context provided with the prompt. As such, they require rich, up-to-date data to deliver accurate, tailored results — and efficiently gathering this confidential knowledge requires search. Search-based RAG delivers this context automatically and eliminates the need to build a bespoke LLM and constantly retrain it on ever-changing internal data.
Attack Discovery uniquely leverages the Search AI platform to sort and identify which alert details should be evaluated by the LLM. By querying the rich context contained within Elastic Security alerts with the hybrid search capabilities of Elasticsearch, the solution retrieves the most relevant data to provide to the LLM and instructs it to identify and prioritize the few attacks accordingly. This includes data such as host and user risk scores, asset criticality scores, alert severities, descriptions and alert reasons.
“As a lean organization, we do not operate a traditional SOC team, so the ability to secure our assets faster using our existing team and generative AI is very exciting,” said Kadir Burak Mavzer, Cloud Security team lead at Bolt. “We've already seen great results with Elastic AI Assistant and are looking forward to using Attack Discovery soon.”
“Attack Discovery is a transformative step towards solving the ongoing cybersecurity workforce shortage. Investigations that would have taken entire teams can now be investigated by a single analyst in less time,” said Ken Buckler, information security research director at EMA. “Attack Discovery will provide analysts and incident responders a significant advantage over existing log analysis focused solutions.”
“The attacks companies face are as constant as they are sophisticated, and with no lever to slow the deluge of signals, most security teams struggle to keep their heads above water,” said Santosh Krishnan, general manager of Security at Elastic. “Nearly 20% of our security customers already use our AI Assistant to boost team efficiency. Similarly, Attack Discovery will power productivity and supplement practitioner knowledge to speed up threat detection, investigation, and response. It helps your people — and SOC — succeed.”
Many SOCs have 1000s of alerts to sift through daily. Much of this work is dull, time-intensive, and error-prone. Elastic Security removes the need for such manual effort. Attack Discovery triages out the false positives and maps the remaining strong signals to discrete attack chains, showing how related alerts are part of an attack chain. Attack Discovery uses LLMs to evaluate alerts, taking into consideration severity, risk scores, asset criticality and more. By delivering this accurate and fast triage, analysts can spend less time sifting through alerts and more time investigating and addressing threats.
Since its release in 2019, Elastic Security has grown to include some of the industry’s most advanced analytics capabilities, including 100+ prebuilt ML-based anomaly detection jobs to detect previously unknown threats. Last year, Elastic introduced Elastic AI Assistant for Security to help SOC analysts with rule authoring, alert summarization, and workflow and integration recommendations.
Attack Discovery will be available to all customers with an Enterprise license as part of the Elastic 8.14 release.
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.