Five Documentation Mistakes That Could Derail Your AI Compliance

Documentation requirements under the AI Act are extensive, and many organizations underestimate the effort required. Here are the most common mistakes we see:

1. Incomplete Training Data Records You need to document not just what data you used, but how it was collected, cleaned, and validated. Many teams skip the provenance details that auditors will ask about.

2. Missing Risk Assessment Updates Risk assessments aren't one-time exercises. As your system evolves, so do the risks. Failing to maintain current assessments is a frequent gap.

3. Vague Technical Documentation Generic descriptions won't suffice. Auditors expect specific details about model architecture, performance metrics, and known limitations.

4. No Clear Version Control When you update your AI system, can you prove what changed and why? Version control for documentation is just as important as for code.

5. Siloed Documentation If your technical docs live in one system, risk assessments in another, and policies in a shared drive somewhere, you'll struggle to present a coherent compliance story.