How to Prepare AI Act Technical Documentation
If your AI system is classified as High-Risk under the EU AI Act, a standard GitHub README.md is not enough.
Before placing a High-Risk AI system on the EU market, you must pass a conformity assessment. The core artifact in that process is Technical Documentation under Annex IV. This is an audit-grade package proving your system is safe, transparent, and robust. Missing or incomplete documentation can delay market access and expose your business to major enforcement risk.
Here is the implementation blueprint developers, CTOs, and compliance teams should use to prepare EU AI Act Technical Documentation.
1. System description (architecture and purpose)
Auditors must understand what your AI does, where it is used, and how it is built. Black-box claims are not enough.
- Intended purpose: Define the specific, legally relevant use case (for example: "Automated resume screening for initial candidate filtering").
- Architecture diagrams: Include high-level and low-level diagrams covering cloud services, data flows, model calls, APIs, and microservices.
- Hardware and software specifications: Document runtime requirements and framework versions (for example PyTorch/TensorFlow versions).
- Third-party components: If you rely on foundation model providers via API, document dependencies, assumptions, and controls around upstream behavior.
2. Training data (provenance and governance)
Data quality and provenance are among the most scrutinized elements in EU AI Act enforcement.
- Data provenance: Record where data came from (scraped, licensed, generated, internal sources).
- Data quality and cleaning: Keep logs of preprocessing, normalization, deduplication, and filtering steps.
- Bias mitigation: Document statistical testing and mitigation methods for protected characteristics.
- GDPR overlap: If personal data appears in training or evaluation sets, document lawful basis, retention, and data-subject controls.
3. Evaluation (metrics and human oversight)
You must prove performance and control in both normal and adverse conditions.
- Performance metrics: Record metrics such as precision, recall, F1, and baseline/benchmark outcomes.
- Robustness and cybersecurity: Include tests for adversarial input, prompt injection, data poisoning, and edge-case behavior.
- Human-in-the-loop (HITL): Document operator interfaces for supervision, intervention, overrides, and emergency kill-switch flows.
4. Risk management (identification and mitigation)
Under Article 9, High-Risk AI requires a continuous risk management system. Treat this as a living operational process, not a static PDF.
- Foreseeable risks: Catalogue potential impacts on health, safety, and fundamental rights.
- Mitigation strategies: Map each identified risk to concrete safeguards in code, product design, and operations.
- Post-market monitoring: Define telemetry, event logging, and alerting pipelines for production behavior and incident response.
Stop guessing. Determine your exact compliance requirements.
Annex IV documentation can require hundreds of engineering hours. Before committing to that workload, validate whether your system is actually High-Risk.
We built a developer-first tool that maps your app's features against EU AI Act criteria in under 60 seconds. Know your tier, know your required controls, and prioritize your roadmap with confidence.
Run your risk classification first
Start with a fast preliminary classification, then scope documentation and controls based on your likely tier.