AI Governance

AI Governance is the set of policies, processes, roles and controls by which an organization manages its AI systems. 2025 marks a turning point: from experimental frameworks to operational compliance. Organizations face the challenge of turning compliance frameworks into working governance structures. The goal is not only to manage risks, but also to build trust with stakeholders and create value through reliable AI.

Effective AI governance follows the three lines model. The first line consists of operational ownership: a product owner for each AI use case, development teams and business owners. The second line forms independent control: risk management, compliance and legal. The third line is internal audit that periodically assesses whether governance works effectively. C-level sponsorship through an AI Ethics Board or Chief AI Officer provides strategic direction.

The NIST AI Risk Management Framework provides a practical starting point with four functions: Govern, Map (identify and understand risks), Measure (measure and evaluate) and Manage (manage and mitigate). This cyclical approach ensures continuous attention to risks throughout the AI lifecycle. Effective risk management goes beyond technical metrics and also includes bias, fairness, privacy and security.

The EU AI Act requires comprehensive documentation for high-risk AI systems. This includes technical documentation about the model, training data provenance, evaluation results and known limitations. An AI register forms the basis: per system name, purpose, risk classification, responsible party, data sources, model version and monitoring metrics. Model Cards and AI Bill of Materials are practical formats to structure this.

Different assessments are required depending on the type of AI system. A DPIA (Data Protection Impact Assessment) focuses on privacy risks and is mandatory under GDPR. A FRIA (Fundamental Rights Impact Assessment) is broader and assesses impact on all fundamental rights, mandatory for high-risk deployers under the EU AI Act. Additionally, there are conformity assessments that providers must conduct for high-risk systems.

Enforcement is coordinated by the European AI Office at EU level and national supervisors in each member state. In the Netherlands these are the Data Protection Authority for privacy aspects and the Algorithm Supervisor for algorithmic systems. Sectoral supervisors such as AFM and DNB also have AI in their mandate. Incident reporting is mandatory within 15 days for serious malfunctions.

Audit readiness means your organization can demonstrate at any time that AI governance works effectively. This requires complete documentation, real-time monitoring dashboards, clear escalation paths and trained personnel. Conduct regular internal audits to identify gaps before external supervisors arrive. A mock audit helps test the maturity of your governance.

Various tools and templates are available to support AI governance. Microsoft's Responsible AI Impact Assessment template provides a practical format for impact assessments. The Dutch Algorithm Register shows how you can document AI transparently. ISO/IEC 42001 provides a formal structure for an AI management system. Standard formats for Model Cards and AI Bill of Materials help with consistent documentation.