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> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Evaluating LLMs for SOC automation: a benchmark framework — Elastic Security Labs

**[Elastic Security Labs](https://daily.dev/sources/elastic-security-labs)** · 20 min read · 1 upvotes · 0 comments

## Summary

Elastic Security Labs describes a benchmarking framework for evaluating LLMs in agentic Security Operations Center (SOC) workflows. Unlike generic leaderboards that score text quality, this framework seeds a realistic synthetic intrusion (the Chrysalis backdoor campaign) into a live Elastic deployment, runs every model through the same agent with identical tools and prompts, and captures full execution traces — every tool call, parameter, and result. Three evaluation suites are covered: an agent suite testing seven capability categories (alert triage, entity analytics, threat hunting, detection rule authoring, workflow authoring, workflow triggering, and multi-step response); Attack Discovery, which grades how well models correlate raw alerts into coherent attack narratives; and Automatic Migration, which measures how faithfully models port Splunk detection rules into Elastic. Scoring is blind (models anonymized before judging), caps unsubstantiated answers at 6/10, and separates reliability failures from quality scores. The key finding: model rankings flip depending on the capability, making per-task evaluation essential for production SOC deployments.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.elastic.co/security-labs/llm-benchmarking-agentic-soc>

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---

Tags: [#security](https://daily.dev/tags/security), [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#elk](https://daily.dev/tags/elk)

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