<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Secure Design on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/secure-design/</link><description>Recent content in Secure Design on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 20 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/secure-design/index.xml" rel="self" type="application/rss+xml"/><item><title>Building Secure AI Applications: A Defense-in-Depth Approach</title><link>https://ai-blog.noorshomelab.dev/ai-security-guide-2026/secure-ai-application-design/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/ai-security-guide-2026/secure-ai-application-design/</guid><description>&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;Welcome back, future AI security champions! In our previous chapters, we delved into specific vulnerabilities like prompt injection, jailbreaks, data poisoning, and tool misuse. We learned to identify these threats and even explored some initial mitigation techniques. But how do we tie all of this together into a cohesive, robust security strategy for an entire AI application?&lt;/p&gt;
&lt;p&gt;That&amp;rsquo;s precisely what we&amp;rsquo;ll tackle in this chapter: &lt;strong&gt;Building Secure AI Applications with a Defense-in-Depth Approach&lt;/strong&gt;. We&amp;rsquo;ll move beyond individual fixes to understanding how to design AI systems that are inherently more resilient against a wide array of attacks. Our goal is to equip you with the knowledge to architect AI applications that are not just functional, but truly &lt;em&gt;production-ready&lt;/em&gt; – meaning they can withstand sophisticated threats in the real world.&lt;/p&gt;</description></item></channel></rss>