Organizations handling security questionnaires often struggle with the provenance of AI‑generated answers. This article explains how to build a transparent, auditable evidence pipeline that captures, stores, and links every piece of AI‑produced content to its source data, policies, and justification. By combining LLM orchestration, knowledge‑graph tagging, immutable logs, and automated compliance checks, teams can provide regulators with a verifiable trail while still enjoying the speed and accuracy that AI delivers.

Multi‑modal large language models (LLMs) can read, interpret, and synthesize visual artifacts—diagrams, screenshots, compliance dashboards—turning them into audit‑ready evidence. This article explains the technology stack, workflow integration, security considerations, and real‑world ROI of using multi‑modal AI to automate visual evidence generation for security questionnaires.