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Elastic Changes the SIEM Game with AI-Driven Security Analytics

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.

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Elastic Changes the SIEM Game with AI-Driven Security Analytics

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.

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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 ...