EU AI Act: Translating Legal Requirements into Infrastructure Specifications

The EU AI Act entered into force in 2025, with obligations for high-risk AI systems taking effect in phases through 2027. For infrastructure teams, the challenge is translating legal articles into platform capabilities.

High-Risk AI System Requirements → Infrastructure Specs

Risk Management System (Article 9)

Legal: Continuous, iterative risk management throughout the AI system lifecycle.

Infrastructure: Automated risk scoring in the ML pipeline. Model monitoring dashboards with drift detection, fairness metrics, and anomaly alerts. Version-controlled risk assessments tied to model versions.

Data Governance (Article 10)

Legal: Training data must be relevant, representative, and free of errors. Statistical properties must be examined.

Infrastructure: Data validation pipelines (Great Expectations, Deequ). Automated bias detection in training datasets. Data profiling integrated into CI/CD. Schema enforcement and drift monitoring.

Technical Documentation (Article 11)

Legal: Detailed documentation of the AI system before market placement.

Infrastructure: Auto-generated model cards from the ML pipeline. Experiment tracking (MLflow, Weights & Biases) with mandatory metadata. Infrastructure-as-Code documentation that maps to compliance requirements.

Record-Keeping (Article 12)

Legal: Automatic logging of events throughout the system's lifecycle.

Infrastructure: Centralized logging with tamper-proof storage. Inference logging with input/output capture. Model serving audit trails. Minimum retention periods enforced at the storage layer.

Human Oversight (Article 14)

Legal: AI systems must allow effective human oversight.

Infrastructure: Human-in-the-loop inference endpoints. Confidence threshold routing (low confidence → human review queue). Kill switches and model rollback automation. Dashboard visibility into real-time model behavior.

The Platform Engineering Approach

Don't solve these requirements per-model. Build a platform that makes compliance the default for every AI system deployed on it. Golden paths for model development that embed AI Act requirements from experiment to production.