<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Real‑Time Monitoring on Smart Automation for Questionnaires &amp; Compliance</title><link>https://blog.procurize.ai/categories/realtime-monitoring/</link><description>Recent content in Real‑Time Monitoring on Smart Automation for Questionnaires &amp; Compliance</description><generator>Hugo</generator><language>en</language><atom:link href="https://blog.procurize.ai/categories/realtime-monitoring/index.xml" rel="self" type="application/rss+xml"/><item><title>Explainable AI Powered Real Time Compliance Policy Drift Detection Using Temporal Graph Neural Networks</title><link>https://blog.procurize.ai/explainable-ai-powered-real-time-compliance-policy-drift-det/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.procurize.ai/explainable-ai-powered-real-time-compliance-policy-drift-det/</guid><description>&lt;h1 id="explainable-ai-powered-real-time-compliance-policy-drift-detection-using-temporal-graph-neural-networks">Explainable AI Powered Real Time Compliance Policy Drift Detection Using Temporal Graph Neural Networks&lt;/h1>
&lt;h2 id="introduction">Introduction&lt;/h2>
&lt;p>Enterprises are under constant pressure to keep their security and regulatory policies aligned with an ever‑changing landscape of standards, internal audits, and third‑party requirements. &lt;strong>Policy drift&lt;/strong>—the gradual divergence between documented policies and the actual configuration of systems—often goes unnoticed until a compliance audit surfaces costly gaps.&lt;/p>
&lt;p>Traditional drift detection relies on periodic scans and rule‑based diff tools. While useful, they suffer from three critical limitations:&lt;/p></description></item></channel></rss>