This article introduces a novel federated prompt engine that enables secure, privacy‑preserving automation of security questionnaires for multiple tenants. By combining federated learning, encrypted prompt routing, and a shared knowledge graph, organizations can reduce manual effort, maintain data isolation, and continuously improve answer quality across diverse regulatory frameworks.

This article dives deep into Procurize AI’s novel Federated Retrieval‑Augmented Generation (RAG) engine, designed to harmonize answers across multiple regulatory frameworks. By marrying federated learning with RAG, the platform delivers real‑time, context‑aware responses while preserving data privacy, cutting turnaround time, and improving answer consistency for security questionnaires.

This article explores how privacy‑preserving federated learning can revolutionize security questionnaire automation, allowing multiple organizations to collaboratively train AI models without exposing sensitive data, ultimately accelerating compliance and reducing manual effort.

This article explores a novel architecture that combines zero‑trust principles with a federated knowledge graph to enable secure, multi‑tenant automation of security questionnaires. You’ll discover the data flow, privacy guarantees, AI integration points, and practical steps to implement the solution on the Procurize platform.