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 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.

This article dives deep into prompt engineering strategies that make large language models produce precise, consistent, and auditable answers for security questionnaires. Readers will learn how to design prompts, embed policy context, validate outputs, and integrate the workflow into platforms like Procurize for faster, error‑free compliance responses.

This article introduces a practical blueprint that merges Retrieval‑Augmented Generation (RAG) with adaptive prompt templates. By linking real‑time evidence stores, knowledge graphs, and LLMs, organizations can automate security questionnaire responses with higher accuracy, traceability, and auditability, while keeping compliance teams in control.