How Elastic's AI-Powered Approach Is Reshaping Modern SIEM Strategies

The traditional Security Information and Event Management landscape is undergoing a fundamental transformation. Elastic, the Search AI Company, has unveiled a paradigm shift in how security operations teams manage the overwhelming volume of security alerts that plague modern SOCs—introducing Attack Discovery, a groundbreaking capability within its Elastic Security platform.

The Core Challenge: Alert Fatigue vs. Real Threats

Security teams face an unrelenting problem: thousands of daily alerts competing for attention, yet only a fraction represent genuine threats. This creates a critical bottleneck. Analysts spend countless hours manually sifting through noise, configuring detection rules, and investigating false positives—all while sophisticated attacks slip through the cracks. The workforce shortage in cybersecurity compounds this challenge, leaving lean security operations stretched thin.

Attack Discovery: Automating Alert Triage at Scale

Rather than forcing analysts to manually parse hundreds of daily alerts, Attack Discovery leverages Elastic’s Search AI platform to instantly filter and prioritize threats. The solution works by combining search technology with retrieval augmented generation (RAG) to intelligently rank alerts based on multiple factors: host and user risk scores, asset criticality, alert severity levels, and contextual descriptions.

The result is striking—what previously required teams of analysts now reduces to a single button click, instantly surfacing only the attacks that matter. Attack Discovery maps related alerts to discrete attack chains, revealing how seemingly disconnected signals form a cohesive threat narrative.

Why Search-Based RAG Matters for Security AI

Large language models are only as effective as the data they process. Traditional LLM approaches struggle because they rely on static training data that quickly becomes outdated. Elastic’s approach is fundamentally different: it pairs LLMs with real-time search capabilities, ensuring that the AI evaluates alerts using the most current, relevant context available within your environment.

By querying Elasticsearch’s hybrid search capabilities, Attack Discovery automatically retrieves the precise data an LLM should analyze—eliminating the need to build custom models or constantly retrain systems as your security landscape evolves. This architecture delivers accuracy without the operational overhead.

Practical Impact: From Theory to Real-World Results

Organizations already using Elastic Security’s AI Assistant report measurable efficiency gains. Kadir Burak Mavzer, Cloud Security team lead at Bolt, noted that as a lean operation relying on existing teams augmented by generative AI, Attack Discovery offers an exciting path to faster asset protection.

Industry analysts echo this sentiment. Ken Buckler, information security research director at EMA, characterized Attack Discovery as “transformative” for solving the ongoing cybersecurity skills shortage—investigations that once required entire teams can now be handled by individual analysts in significantly less time.

Market Readiness and Broader Elastic Security Capabilities

Attack Discovery arrives as the latest evolution of Elastic Security, which has matured since its 2019 launch to include over 100 prebuilt machine learning-based anomaly detection jobs for identifying previously unknown threats. The platform already powers AI-assisted workflows through Elastic AI Assistant for Security, which helps analysts with rule authoring, alert summarization, and integration recommendations.

The solution becomes available immediately to all Enterprise license holders through the Elastic 8.14 release, representing the culmination of Elastic’s strategic pivot toward AI-driven security analytics.

Why This Matters for the Future of SIEM

Santosh Krishnan, general manager of Security at Elastic, frames the challenge plainly: “Nearly 20% of our security customers already use our AI Assistant to boost team efficiency.” Attack Discovery extends this productivity advantage across the entire alert lifecycle—detection, investigation, and response.

For security teams drowning in false positives and alert noise, the shift from counting alerts to prioritizing actual attacks represents more than a feature update. It’s a fundamental reimagining of how modern SOCs should operate—powered by AI that understands context, not just patterns.