<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Threat-Investigation on CuraSec</title><link>https://curasec.metacog.co.kr/tags/threat-investigation/</link><description>Recent content in Threat-Investigation on CuraSec</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 13 Jul 2026 14:30:14 +0000</lastBuildDate><atom:link href="https://curasec.metacog.co.kr/tags/threat-investigation/index.xml" rel="self" type="application/rss+xml"/><item><title>SherAgent: LLM-Powered Provenance Graph Attack Investigation</title><link>https://curasec.metacog.co.kr/insights/2026-07-13-sheragent-scaling-attack-investigation-in-the-wild-via-llm-e/</link><pubDate>Mon, 13 Jul 2026 14:30:14 +0000</pubDate><guid>https://curasec.metacog.co.kr/insights/2026-07-13-sheragent-scaling-attack-investigation-in-the-wild-via-llm-e/</guid><description>&lt;ul>
&lt;li>&lt;strong>Engineer — Skip&lt;/strong>&lt;/li>
&lt;li>&lt;strong>SOC/IR — Learn:&lt;/strong> SherAgent demonstrates a 31–64% improvement in automated attack investigation success rates using LLM-driven provenance graph backtracking — useful context for teams evaluating or building AI-assisted triage workflows, though no production tool or IOCs are released here.&lt;/li>
&lt;li>&lt;strong>Leader — Learn:&lt;/strong> Research from a real SOC environment shows LLM-assisted alert triage meaningfully reduces the manual investigation backlog; relevant background for leaders assessing AI tooling investments in detection and response.&lt;/li>
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