This article introduces a novel Predictive Compliance Gap Forecasting Engine that blends generative AI, federated learning, and knowledge‑graph enrichment to forecast upcoming security questionnaire items. By analyzing historical audit data, regulatory roadmaps, and vendor‑specific trends, the engine predicts gaps before they appear, enabling teams to prepare evidence, policy updates, and automation scripts in advance, dramatically reducing response latency and audit risk.

This article explains the concept of intent‑based routing for security questionnaires, how real‑time risk scoring drives automated answer selection, and why integrating a unified AI platform reduces manual effort while boosting compliance accuracy. Readers will learn the architecture, key components, implementation steps, and real‑world benefits.