This article introduces a novel synthetic data augmentation engine designed to empower Generative AI platforms like Procurize. By creating privacy‑preserving, high‑fidelity synthetic documents, the engine trains LLMs to answer security questionnaires accurately without exposing real customer data. Learn the architecture, workflow, security guarantees, and practical deployment steps that reduce manual effort, improve answer consistency, and maintain regulatory compliance.

Security questionnaires are a bottleneck for many SaaS providers, demanding precise, repeatable answers across dozens of standards. By generating high‑quality synthetic data that mirrors real audit responses, organizations can fine‑tune large language models (LLMs) without exposing sensitive policy text. This article walks through a complete synthetic‑data‑centric pipeline, from scenario modeling to integration with a platform like Procurize, delivering faster turnaround, consistent compliance, and a safe training loop.