<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Detection Engineering on FIR Risk Advisory | Risk Intelligence. Engineered.</title><link>https://firrisk.ai/tags/detection-engineering/</link><description>Recent content in Detection Engineering on FIR Risk Advisory | Risk Intelligence. Engineered.</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 29 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://firrisk.ai/tags/detection-engineering/index.xml" rel="self" type="application/rss+xml"/><item><title>INTEL-24: Breadth, Not Depth</title><link>https://firrisk.ai/intel/intel-24-breadth-not-depth/</link><pubDate>Mon, 29 Jun 2026 00:00:00 +0000</pubDate><guid>https://firrisk.ai/intel/intel-24-breadth-not-depth/</guid><description>&lt;h2 id="the-intel" class="heading "&gt;The INTEL&lt;a href="#the-intel" aria-labelledby="the-intel"&gt;








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&lt;p&gt;&lt;strong&gt;Generative AI is not unlocking novel attacks. It is scaling the ones we already know. The 2026 DBIR studied 793 unique threat actors using AI — and fewer than 1% reached a high or critical capability tier. AI today is a breadth multiplier, not a depth multiplier.&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;When Verizon, working with Anthropic, examined how attackers actually use AI, the picture was not the super-attacker of the hype cycle. The median attacker applied AI across roughly 15 well-documented techniques — some reached 40-50 — each backed by a median of 55 already-known malware examples. Fewer than 2.5% of the observed techniques were genuinely rare. The story is volume and velocity over well-trodden ground, not a leap into the unknown.&lt;/p&gt;</description></item></channel></rss>