Inside Elastic's agentic SOC: How we took AI alert triage from 60% to 92% accuracy

Elastic's InfoSec team has revolutionized their Security Operations Center (SOC) efficiency through the implementation of an agentic AI pipeline that has boosted alert triage accuracy from 60% to an impressive 92%. Rather than solely relying on raw alert data, the team leverages Elastic Workflows and Agent Builder to equip AI agents with a wealth of contextual information.

This contextual data includes historical case data, internal investigation guides, and user risk information sourced from Workday. By transforming AI summaries from untrusted "slop" into actionable intelligence, analysts can confidently verify and act upon the information. The architecture employs three specialized agents - Pattern Finder, L1 Investigation, and Summarizer - working in a synchronized pipeline to optimize resource usage and speed.

The Pattern Finder identifies historical trends and analyzes analyst feedback loops, the L1 Investigator conducts targeted external queries to reach a verdict, and the Summarizer formats the final report for seamless integration into existing communication channels like Slack and Kibana. This automated ecosystem empowers analysts to swiftly close cases, often with a single click, by integrating directly into their existing workflows. The system also maintains a transparent feedback loop, facilitating continuous model improvement.