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 AI‑driven real‑time evidence orchestration engine that continuously syncs policy changes, extracts relevant proof, and auto‑populates security questionnaire responses, delivering speed, accuracy, and auditability for modern SaaS vendors.

Retrieval‑Augmented Generation (RAG) combines large language models with up‑to‑date knowledge sources, delivering accurate, contextual evidence at the moment a security questionnaire is answered. This article explores RAG architecture, integration patterns with Procurize, practical implementation steps, and security considerations, equipping teams to cut response time by up to 80 % while maintaining audit‑grade provenance.

This article explores a next‑generation AI platform that centralizes security questionnaires, compliance audits, and evidence management. By combining real‑time knowledge graphs, generative AI, and seamless tool integrations, the solution reduces manual workload, accelerates response times, and ensures audit‑grade accuracy for modern SaaS companies.

Organizations increasingly rely on AI to answer security questionnaires, but prompt engineering remains a bottleneck. A composable prompt marketplace lets security, legal, and engineering teams share, version, and reuse vetted prompts. This article explains the concept, architectural patterns, governance models, and practical steps to build a marketplace inside Procurize, turning prompt work into a strategic asset that scales with compliance demands.