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 explores the strategy of fine‑tuning large language models on industry‑specific compliance data to automate security questionnaire responses, reduce manual effort, and maintain auditability within platforms like Procurize.

In modern SaaS enterprises, security questionnaires are a major bottleneck. This article introduces a novel AI solution that uses Graph Neural Networks to model the relationships between policy clauses, historical answers, vendor profiles and emerging threats. By turning the questionnaire ecosystem into a knowledge graph, the system can automatically assign risk scores, recommend evidence, and surface high‑impact items first. The approach cuts response time by up to 60 % while improving answer accuracy and audit readiness.

This article explores the design and implementation of an immutable ledger that records AI‑generated questionnaire evidence. By combining blockchain‑style cryptographic hashes, Merkle trees, and retrieval‑augmented generation, organizations can guarantee tamper‑proof audit trails, satisfy regulatory demands, and boost stakeholder confidence in automated compliance processes.

This article explores the emerging multi modal AI approach that enables automated extraction of textual, visual, and code evidence from diverse documents, accelerating security questionnaire completion while maintaining compliance and auditability.