This article explores a novel approach that combines large language models, live risk telemetry, and orchestration pipelines to automatically generate and adapt security policies for vendor questionnaires, reducing manual effort while maintaining compliance fidelity.

This article explores a novel, ontology‑driven prompt engineering architecture that aligns disparate security questionnaire frameworks such as [SOC 2](https://secureframe.com/hub/soc-2/what-is-soc-2), [ISO 27001](https://www.iso.org/standard/27001), and [GDPR](https://gdpr.eu/). By building a dynamic knowledge graph of regulatory concepts and leveraging smart prompt templates, organizations can generate consistent, auditable AI answers across multiple standards, reduce manual effort, and improve compliance confidence.

This article introduces a novel semantic‑graph‑based auto‑linking engine that instantly maps supporting evidence to security questionnaire answers in real time. By leveraging AI‑enhanced knowledge graphs, natural‑language understanding, and event‑driven pipelines, organizations can cut response latency, improve auditability, and maintain a living evidence repository that evolves with policy changes.

This article explores a new AI‑powered approach called Contextual Evidence Synthesis (CES). CES automatically gathers, enriches, and assembles evidence from multiple sources—policy docs, audit reports, and external intel—into a coherent, auditable answer for security questionnaires. By combining knowledge‑graph reasoning, retrieval‑augmented generation, and fine‑tuned validation, CES delivers real‑time, precise responses while maintaining a full change‑log for compliance teams.

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.