<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Network-Security on A Field CTO Perspective</title><link>https://gaberger.github.io/tags/network-security/</link><description>Recent content in Network-Security on A Field CTO Perspective</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sun, 26 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://gaberger.github.io/tags/network-security/index.xml" rel="self" type="application/rss+xml"/><item><title>The deterministic half of neuro-symbolic AI already exists. It's called network verification.</title><link>https://gaberger.github.io/2026/07/deterministic-half-of-neuro-symbolic-ai/</link><pubDate>Sun, 26 Jul 2026 00:00:00 +0000</pubDate><guid>https://gaberger.github.io/2026/07/deterministic-half-of-neuro-symbolic-ai/</guid><description>&lt;p>Everybody&amp;rsquo;s suddenly got a take on neuro-symbolic AI. Inference for pattern recognition, symbolic logic paired in for reasoning you can actually trust, and the EU AI Act&amp;rsquo;s enforcement phase lighting a fire under all of it because &amp;ldquo;the model said so&amp;rdquo; stopped being an acceptable audit trail.&lt;/p>
&lt;p>The pitch is real. Inference is powerful and unreliable, a statistical guess dressed up as an answer. Deterministic, mathematical models are reliable and narrow. Combining them, having the deterministic side check inference&amp;rsquo;s work, is turning into the actual path to AI you can trust in production. What&amp;rsquo;s less talked about: in networking, the deterministic half of that equation isn&amp;rsquo;t something you&amp;rsquo;d have to build. It already exists, and it&amp;rsquo;s been hardening in production for 7 years.&lt;/p></description></item></channel></rss>