<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data Governance on Formize.com Blog</title><link>https://blog.formize.com/tags/data-governance/</link><description>Recent content in Data Governance on Formize.com Blog</description><generator>Hugo</generator><language>en</language><atom:link href="https://blog.formize.com/tags/data-governance/index.xml" rel="self" type="application/rss+xml"/><item><title>Accelerating Multi‑Modal Generative AI Data Lineage and Provenance with Formize</title><link>https://blog.formize.com/accelerating-multi-modal-generative-ai-data-lineage-and-prov/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.formize.com/accelerating-multi-modal-generative-ai-data-lineage-and-prov/</guid><description>&lt;h1 id="accelerating-multimodal-generative-ai-data-lineage-and-provenance-with-formize">Accelerating Multi‑Modal Generative AI Data Lineage and Provenance with Formize&lt;/h1>
&lt;p>Generative AI models are no longer limited to a single data type. Modern systems ingest &lt;strong>text, images, audio, video, and even 3‑D meshes&lt;/strong> to produce rich, cross‑modal content. While this unlocks unprecedented creativity, it also introduces a tangled web of data dependencies that can quickly become a compliance nightmare.&lt;/p>
&lt;p>&lt;strong>Formize&lt;/strong>—the low‑code, blockchain‑backed workflow engine—offers a unified approach to capture, track, and verify every artifact in a multi‑modal pipeline. In this article we’ll:&lt;/p></description></item></channel></rss>