From 8b5e0a29db2078cee6403d72cf2b9478e93da5f3 Mon Sep 17 00:00:00 2001
From: Benjamin Ironside Goldstein
 <91905639+benironside@users.noreply.github.com>
Date: Mon, 16 Jun 2025 13:53:13 -0700
Subject: [PATCH] Update attack-discovery.asciidoc (#6881)

(cherry picked from commit eda6167de8dd8b4edae30b4cd5bbb39e577b762a)
---
 docs/AI-for-security/attack-discovery.asciidoc | 2 --
 1 file changed, 2 deletions(-)

diff --git a/docs/AI-for-security/attack-discovery.asciidoc b/docs/AI-for-security/attack-discovery.asciidoc
index 646f371c83..0235805c19 100644
--- a/docs/AI-for-security/attack-discovery.asciidoc
+++ b/docs/AI-for-security/attack-discovery.asciidoc
@@ -7,8 +7,6 @@
 :frontmatter-tags-content-type: [overview]
 :frontmatter-tags-user-goals: [get-started]
 
-preview::["This feature is in technical preview. It may change in the future, and you should exercise caution when using it in production environments. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of GA features."]
-
 Attack Discovery leverages large language models (LLMs) to analyze alerts in your environment and identify threats. Each "discovery" represents a potential attack and describes relationships among multiple alerts to tell you which users and hosts are involved, how alerts correspond to the MITRE ATT&CK matrix, and which threat actor might be responsible. This can help make the most of each security analyst's time, fight alert fatigue, and reduce your mean time to respond.
 
 For a demo, refer to the following video.