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AI audit for enterprise CMS

Applied perspective — not legal advice

This page connects the standards in this library to a concrete domain: AI inside an enterprise CMS. It reflects how I approach such engagements as an external auditor; it is general information, not legal advice.

Modern enterprise CMS platforms — especially Adobe Experience Manager (AEM / AEMaaCS) — now ship AI everywhere: in-editor co-pilots, intelligent DAM, RAG-powered intranet chatbots, generative content workflows and increasingly agentic automation. Each of these is an AI system in the sense of the EU AI Act, and each adds risk that traditional CMS security reviews don't cover. That gap is exactly where AI governance meets web engineering — and where I work.

The audit process (six steps)

Derived from the NIST AI RMF (GOVERN · MAP · MEASURE · MANAGE) and aligned with ISO/IEC 42001 and the EU AI Act:

  1. Scope & classify — inventory every AI feature in the CMS (co-pilot, DAM AI, chatbot, recommendations, agents) and classify each by EU AI Act risk tier and business criticality.
  2. Map — document each system's purpose, data flows (incl. what content/DAM assets it can read), model providers and foreseeable misuse. (NIST MAP, EU AI Act Art. 9.)
  3. Assess — evaluate against the applicable controls: data governance, transparency, human oversight, bias, robustness and security (ISO/IEC 42001 Annex A; EU AI Act Arts. 10–15).
  4. Test — red-team the AI surface: prompt injection through page/DAM content, data leakage, output-handling flaws, and agentic overreach (OWASP Top 10 for LLM & Agentic Applications; MITRE ATLAS).
  5. Evidence & report — produce the audit trail: model cards, an AI-SBOM, a living risk register and a findings report mapped to each framework.
  6. Govern & monitor — stand up ongoing monitoring so the picture stays current as models, content and regulations change — the same discipline this watch library demonstrates.

Common AI risks specific to enterprise CMS

  • Prompt injection via content — untrusted page or DAM content steering an in-editor co-pilot or chatbot.
  • Data leakage — a RAG chatbot surfacing restricted DAM assets or unpublished content.
  • Unreviewed generative output — hallucinated or non-compliant content published at scale.
  • Model-provider supply chain — concentration on a few LLM vendors (relevant to DORA for financial-sector sites).
  • Agentic overreach — content/workflow agents acting beyond their intended authority (CSA Agentic Profile).

Frameworks applied

EU AI Act · NIST AI RMF · ISO/IEC 42001 & 23894 · OWASP Top 10 for LLM & Agentic Applications · MITRE ATLAS · ISO/IEC 27001. See Standards at a glance for how they fit together.

Work with me. I both build AEM / AEMaaCS platforms and audit the AI inside them — a rare combination. More on siegfried-bolz.de (incl. the full AI audit for enterprise CMS process) · LinkedIn · info@siegfried-bolz.de.