This article introduces a novel differential privacy engine that safeguards AI‑generated security questionnaire responses. By adding mathematically provable privacy guarantees, organizations can share answers across teams and partners without exposing sensitive data. We walk through the core concepts, system architecture, implementation steps, and real‑world benefits for SaaS vendors and their customers.

This article explains how differential privacy can be integrated with large language models to protect sensitive information while automating security questionnaire responses, offering a practical framework for compliance teams seeking both speed and data confidentiality.