Governance sounds like a legal problem. Under the EU AI Act, a lot of it lands in the engineering backlog. The Act entered into force in August 2024, and its remaining obligations apply from 2 August 2026. If your system falls in the high-risk tier, think recruitment screening, credit scoring or health diagnostics, you will need technical documentation, logging, human oversight and conformity assessment.
Here is what that means in practice for a development team.
1. Build a model inventory. You cannot govern what you have not catalogued. Record every model in production, its owner, its training data sources and the decisions it influences. Include third-party APIs. A vendor's model is still your deployment risk.
2. Classify by risk tier. The Act defines four tiers: unacceptable, high, limited and minimal. Your controls should scale with the tier. A spam filter needs basic logging. A CV-screening model needs bias testing, documented data lineage and a human override path.
3. Put governance checks in CI/CD. Treat fairness tests like unit tests. Run bias checks across demographic groups before each release, block deployment on failure, and version the results so you can show an auditor what you tested and when.
4. Monitor for drift. Models degrade as input data shifts. Automated drift detection with clear escalation paths turns a regulatory duty into standard observability work.
5. Log decisions, not just errors. Explainability requirements mean you may have to reconstruct why a model produced a specific output for a specific person. Design your logging for that question.
The business case is blunt: fines under the Act reach 35 million euros or 7% of global turnover. The engineering case is better: teams with clear guardrails ship faster because approval stops being a negotiation. For the wider context, including how consent and data privacy fit in, this AI governance framework guide covers the full lifecycle.
You do not have to build the data layer yourself either. Seers provides consent management and privacy tooling through its AI governance solution, which means the data feeding your models is collected with valid authorisation from the start.
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