Context
The EU AI Act is often discussed at a policy level, but engineering teams need implementation-level guidance: which controls belong in data pipelines, model development, evaluation, deployment, and monitoring.
This research translates regulatory intent into an operating model teams can apply without slowing delivery.
Core question
How can teams satisfy high-risk AI obligations while preserving release velocity and model performance?
Practical control map
1) Risk classification at intake
Before model development starts, classify use-cases by potential impact domains (safety, rights, discrimination, economic harm). This determines required evidence depth and approval checkpoints.
2) Data governance controls
- Dataset lineage with source accountability.
- Representativeness and bias checks by segment.
- Documented preprocessing transformations and versioned feature logic.
3) Model development controls
- Reproducible training runs and experiment traceability.
- Evaluation protocols with fairness and robustness thresholds.
- Human oversight definitions for decision-boundary scenarios.
4) Deployment controls
- Pre-release conformity checklist tied to risk class.
- Rollback pathways and incident ownership matrix.
- Logging strategy that supports technical auditability.
5) Post-deployment monitoring
- Drift and performance monitoring with escalation tiers.
- Mandatory re-validation triggers for material model changes.
- User-impact incident review loop feeding policy updates.
Engineering artifact set
A minimum operational package for compliance-ready ML releases:
- Model card (versioned)
- Data sheet (source, quality, limitations)
- Risk register (with mitigation status)
- Evaluation dossier (metrics + slices + failure cases)
- Change log linked to deployment and monitoring outcomes
Early observation
Teams fail less on technical capability and more on missing ownership boundaries. Compliance improves significantly when controls are embedded as release gates in CI/CD rather than maintained as separate documentation projects.
Next work
- Build a reusable control-to-checklist template by risk tier.
- Connect governance events to Time-Money-Carbon decision scoring.
- Validate playbook on real production incidents involving drift and retraining.