In our previous article, we explained what Article 12 requires — the three purposes of logging, the role responsibilities, the biometric exception, and the unresolved GDPR tension. Now the question is: how do you actually build it? Article 12 is a technical construction requirement. That means the answer is not a document — it is an architecture. In this article, we walk through the complete logging system for TalentMatch AI, layer by layer: from infrastructure through event

In our previous article, we explained what Article 11 requires — the 9 chapters of Annex IV, the SME track, and how the technical documentation connects to almost every other obligation in the AI Act. Now the question is: how do you actually build it? Theory is useful. But when you sit down to create your technical documentation, you need a concrete process — not just a list of requirements. In this article, we walk through the entire documentation process step by step, using

Article 10 in Practice: How to Build Compliant Data Governance for High-Risk AI Article 10 tells you what compliant data governance looks like. But knowing the requirements and actually implementing them are two different things. How do you document design decisions in practice? How do you examine datasets for bias? How do you map your data to the stakeholders it affects? This guide answers those questions with a step-by-step methodology you can follow today. We'll use a runn

You've run the analysis. Your AI system is classified as High-Risk . Now what? If you've followed our KI-Verordnung Framework guide, you know the four steps to clarity: 1. Scope Check — Does the AI Act apply? ✓ 2. Risk Classification — What's the risk level? ✓ 3. Role Determination — Provider, Deployer, or both? ✓ 4. Obligations — What must you do? ← You are here For High-Risk AI systems, one of the most critical obligations is Article 9: the Risk Management System (RMS) . In

• Modern LLMs (ChatGPT, Claude, Gemini, Grok) will be better than ever by 2026 — but they aren’t designed for compliance • The problem is no longer obvious errors, but errors that sound convincing • Without source references, an audit trail and project-specific context, LLM responses are unusable for the AI Regulation - Alternatives: Research official sources yourself, use specialised tools with RAG, or consult experts