This article explores a novel AI‑driven approach that automatically maps existing policy clauses to specific security questionnaire requirements. By leveraging large language models, semantic similarity algorithms, and continuous learning loops, companies can slash manual effort, improve answer consistency, and keep compliance evidence up‑to‑date across multiple frameworks.

In a world where security questionnaires multiply and regulatory standards shift at breakneck speed, static check‑lists no longer suffice. This article introduces a novel AI‑driven Dynamic Compliance Ontology Builder—a self‑evolving knowledge model that maps policies, controls, and evidence across frameworks, automatically aligns new questionnaire items, and fuels real‑time, auditable responses within the Procurize platform. Learn the architecture, core algorithms, integration patterns, and practical steps to deploy a living ontology that turns compliance from a bottleneck into a strategic advantage.

In today’s fast‑moving regulatory landscape, static compliance repositories quickly become outdated, leading to slow questionnaire turn‑around and risky inaccuracies. This article explains how a self‑healing compliance knowledge base, driven by generative AI and continuous feedback loops, can automatically detect gaps, generate fresh evidence, and keep security questionnaire answers accurate in real‑time.