Why You Need a Framework, Not Point Fixes
As AI spreads across hiring, marketing, support, credit, and operations, treating each system's compliance as a one-off becomes unmanageable and expensive. An AI governance framework solves this by establishing a single, repeatable way to develop and deploy AI: clear ownership, a consistent risk process, standard documentation, and ongoing monitoring. Done well, it turns the EU AI Act, GDPR, and emerging US rules from a pile of separate obligations into one operating model.
Align to Three Reference Points
You do not need to invent governance from first principles. Three frameworks already define best practice, and they overlap heavily:
- EU AI Act — the legal baseline if you touch the EU market. It mandates a quality management system, risk management, human oversight, and post-market monitoring for high-risk AI. This is your floor, not your ceiling.
- ISO/IEC 42001 — the certifiable AI management-system standard. It gives you an auditable structure (the AIMS) familiar to anyone who has run ISO 27001, and certification signals maturity to customers and regulators.
- NIST AI RMF — a practical four-function model (Govern, Map, Measure, Manage) that is especially useful in the US and translates cleanly into the EU Act's risk-management language.
Build one framework that satisfies all three, and you avoid duplicating effort while staying portable across markets — useful if you also serve the US, as covered in our AI Act for US companies guide.
The Core Components
1. Accountability and roles. Name an executive owner for AI governance and define who signs off on AI systems at each risk level. The EU AI Act's human-oversight requirement (Article 14) makes named responsibility non-negotiable for high-risk systems.
2. AI inventory. You cannot govern what you cannot see. Maintain a living register of every AI system you build or use, its purpose, its data, and its risk tier.
3. Risk classification. Tie each system to the EU AI Act's tiers — prohibited, high, limited, minimal — using a repeatable test. Our free risk classifier operationalises this.
4. Policies. A concise AI policy stating acceptable and prohibited uses, plus standards for data governance, transparency, and human oversight.
5. Documentation standards. A consistent way to produce the Annex IV technical file and instructions for use for high-risk systems.
6. Monitoring and incident response. Post-market monitoring, logging, performance tracking, and a route to detect, report, and remediate serious incidents.
7. Review cadence. A scheduled governance review so the framework keeps pace with new systems and regulatory change.
A Phased Rollout
Do not try to govern everything at once. Phase the rollout: first stand up accountability and the AI inventory; second, run risk classification across the inventory and screen for prohibited uses; third, apply full controls to your highest-risk systems and get their documentation in order; fourth, extend the lighter controls (transparency, basic logging) to limited- and minimal-risk systems; finally, embed the review cadence so the framework is self-sustaining. This sequencing front-loads the work where legal and reputational risk is greatest.
Governance and GDPR Together
AI governance and data protection are deeply intertwined. AI systems consume personal data, make or inform decisions about people, and can trigger Article 22 GDPR rights around automated decision-making. Fold your GDPR programme into the same governance structure — shared inventory, shared accountability, shared review — rather than running two parallel bureaucracies.
GeraCompliance gives you the building blocks: a free risk classifier, policy and documentation templates aligned to the EU AI Act and ISO 42001, and a sprint that stands up a working governance baseline in days rather than months.