Elastic's Agentic SOC Slashes Alert Triage Time from 30 Minutes to Under 3 Minutes
Key Takeaways
- ▸Alert triage time reduced from 30 minutes to under 3 minutes by combining ES|QL queries with specialized AI agents
- ▸Uses only Elastic's native stack (Workflows, Agent Builder, Elastic Inference Service) with no third-party dependencies or external orchestrators
- ▸Prioritizes deterministic, auditable queries for obvious cases to minimize token costs and eliminate LLM failure modes like hallucinations
Summary
Elastic's InfoSec team has built an agentic SOC using AI agents on Elastic Workflows that dramatically reduces security alert investigation time. The system combines deterministic ES|QL queries that close obvious false positives at zero token cost with specialized AI agents that investigate complex alerts across endpoint, cloud, and SaaS domains. A final review agent then assembles findings into Kibana cases for analyst review. The entire pipeline runs on Elastic's native stack—Workflows, Agent Builder, Elastic Inference Service, and Kibana Cases—with no third-party orchestrators and inference routed only to vendors documented with zero data retention policies.
The innovation addresses a critical gap in modern security operations: AI-powered attack timelines have compressed from days to hours, making traditional manual alert triage unsustainable. Rather than hiring more analysts, Elastic's approach automates the investigation work that doesn't require human judgment, freeing analysts to focus on alerts demanding expert decision-making. By prioritizing deterministic queries over LLM calls and using specialized agents for different data domains, the system achieves both speed and cost efficiency.
- Data security ensured by routing inference only to zero data-retention providers with documented training policies
Editorial Opinion
Elastic's layered approach—deterministic queries first, then specialized AI agents for ambiguous cases—establishes a pragmatic template for enterprise security teams seeking to scale without external vendors. This pattern could become industry standard for balancing automation and human expertise, particularly in organizations with mature detection rules. However, effectiveness depends heavily on alert quality; organizations with noisy alert streams may find comparable time savings challenging. The emphasis on data retention policies and architectural self-containment is especially valuable for security teams handling sensitive data.
