<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Metr on AegisGate — Secure Every AI Interaction</title><link>https://aegisgatesecurity.io/tags/metr/</link><description>Recent content in Metr on AegisGate — Secure Every AI Interaction</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Wed, 21 Oct 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://aegisgatesecurity.io/tags/metr/feed.xml" rel="self" type="application/rss+xml"/><item><title>METR Reviewed Anthropic's Internal Security. The Findings Validate Why We Built AegisGate.</title><link>https://aegisgatesecurity.io/blog/metr-anthropic-review-why-runtime-monitoring-is-the-only-answer/</link><pubDate>Wed, 21 Oct 2026 00:00:00 +0000</pubDate><guid>https://aegisgatesecurity.io/blog/metr-anthropic-review-why-runtime-monitoring-is-the-only-answer/</guid><description>On October 20, 2026, METR (Model Evaluation &amp;amp; Threat Research) published their review of the &amp;ldquo;Misalignment in high-stakes settings&amp;rdquo; section in Anthropic&amp;rsquo;s August 2026 Risk Report. The findings are sobering, and they validate — with unusual specificity — exactly the threat model that AegisGate was designed to address.
This is not a hot take. I&amp;rsquo;ve read the METR review. I&amp;rsquo;ve read the footnotes. And then I opened our codebase across all five AegisGate products — Platform, Rampart, Scanner, MCP, and Lens — and mapped every finding to what we actually detect, at the code level.</description></item></channel></rss>