This article explores a novel architecture that combines graph neural networks with Procurize’s AI platform to automatically attribute evidence to questionnaire items, generate dynamic trust scores, and keep compliance responses up‑to‑date as regulatory landscapes evolve. Readers will learn the data model, the inference pipeline, integration points, and practical benefits for security and legal teams.

This article explores a next‑generation architecture that combines Retrieval‑Augmented Generation (RAG), Graph Neural Networks (GNN) and federated knowledge graphs to deliver real‑time, accurate evidence for security questionnaires. Learn the core components, integration patterns, and practical steps to implement a dynamic evidence orchestration engine that reduces manual effort, improves compliance traceability, and adapts instantly to regulatory changes.

This article explores a novel Dynamic Evidence Attribution Engine powered by Graph Neural Networks (GNNs). By mapping relationships between policy clauses, control artifacts, and regulatory requirements, the engine delivers real‑time, accurate evidence suggestions for security questionnaires. Readers will learn the underlying GNN concepts, architectural design, integration patterns with Procurize, and practical steps to implement a secure, auditable solution that dramatically reduces manual effort while enhancing compliance confidence.

In modern SaaS enterprises, security questionnaires are a major bottleneck. This article introduces a novel AI solution that uses Graph Neural Networks to model the relationships between policy clauses, historical answers, vendor profiles and emerging threats. By turning the questionnaire ecosystem into a knowledge graph, the system can automatically assign risk scores, recommend evidence, and surface high‑impact items first. The approach cuts response time by up to 60 % while improving answer accuracy and audit readiness.