Elastic announced Elastic Security now offers expanded cloud detection and response (CDR) capabilities from a single SIEM to reduce tool fragmentation and streamline cloud security.
The additional features include agentless ingestion, cloud asset inventory, extended protections, and graph view that enables out-of-the-box correlation and context enrichment using customers’ existing data.
Legacy security solutions have complex workflows and lack cloud-specific context, making them inadequate for the scale and complexity of cloud environments. Using standalone CDR tools often generates vast amounts of fragmented data, making real-time analysis and threat correlation difficult. To address these challenges, Elastic has eliminated the need for a separate CDR tool by being the first to integrate the CDR capabilities directly into an ElasticAI-driven security analytics solution.
“Increasingly dynamic cloud environments are presenting visibility challenges for security with 44% reporting that threat detection and response is more difficult to conduct in cloud environments," said Dave Gruber, principal security analyst at ESG . “SOC teams need to address this cloud visibility gap by collecting, processing, monitoring, and acting upon information from an assortment of cloud security telemetry sources spanning multiple hyperscale cloud providers. Elastic Security’s vendor-agnostic approach to CDR, with the ability to easily ingest and normalize cloud data out of the box, enables security teams to surface critical insights at the speed and scale of the cloud directly with their SIEM.”
“Over the past two years, Elastic has integrated cloud security and CDR capabilities directly into its AI-driven security analytics solution to enhance how modern organizations detect and respond to threats more effectively,” said Santosh Krishnan, general manager of Security at Elastic. “Our comprehensive approach maximizes efficiency, lowers the total cost of ownership (TCO), and alleviates the burden on security teams. Ultimately, Elastic Security ensures organizations stay ahead of evolving threats while leveraging the full benefits of CDR.”
Support for Elastic Security’s new CDR capabilities is available today.
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...