Manual security questionnaire processes are slow, error‑prone, and often siloed. This article introduces a privacy‑preserving federated knowledge graph architecture that lets multiple companies share compliance insights securely, boost answer accuracy, and cut response times—all while complying with data‑privacy regulations.

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.