This article explores a novel architecture that couples retrieval‑augmented generation, prompt‑feedback cycles, and graph neural networks to let compliance knowledge graphs evolve automatically. By closing the loop between questionnaire answers, audit outcomes, and AI‑driven prompts, organizations can keep their security and regulatory evidence up‑to‑date, reduce manual effort, and boost audit confidence.

This article explores the need for responsible AI governance when automating security questionnaire responses in real time. It outlines a practical framework, discusses risk mitigation tactics, and shows how to combine policy‑as‑code, audit trails, and ethical controls to keep AI‑driven answers trustworthy, transparent, and compliant with global regulations.

Modern SaaS firms face an avalanche of security questionnaires, vendor assessments, and compliance audits. While AI can accelerate answer generation, it also introduces concerns about traceability, change management, and auditability. This article explores a novel approach that couples generative AI with a dedicated version‑control layer and an immutable provenance ledger. By treating each questionnaire response as a first‑class artefact—complete with cryptographic hashes, branching history, and human‑in‑the‑loop approvals—organizations gain transparent, tamper‑evident records that satisfy auditors, regulators, and internal governance boards.

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.

In an environment where vendors face dozens of security questionnaires across frameworks such as [SOC 2](https://secureframe.com/hub/soc-2/what-is-soc-2), [ISO 27001](https://www.iso.org/standard/27001), GDPR and CCPA, generating precise, context‑aware evidence quickly is a major bottleneck. This article introduces an ontology‑guided generative AI architecture that transforms policy documents, control artifacts and incident logs into tailored evidence snippets for each regulatory question. By coupling a domain‑specific knowledge graph with prompt‑engineered large language models, security teams achieve real‑time, auditable responses while maintaining compliance integrity and reducing turnaround time dramatically.