Building an AI governance framework with enterprise architecture

The EU AI Act is now in effect, and organisations worldwide are scrambling to understand what it means for their AI initiatives. Risk classification, transparency requirements, human oversight mandates — the regulatory landscape for AI is complex and evolving. Enterprise architecture is uniquely positioned to provide the governance structure these regulations demand.

Most organisations today have no central view of their AI portfolio. Models are trained by data science teams, deployed by engineering teams, and governed by nobody. Shadow AI — models deployed without formal review or approval — is the norm, not the exception.

Enterprise architecture provides the natural framework for AI governance because it already answers the questions regulators are asking: What systems exist? What data do they process? Who is responsible? What are the dependencies? What are the risks?

ArchNova's AI Strategy capability extends the traditional EA metamodel to include AI-specific constructs: models, datasets, training pipelines, inference endpoints, and governance workflows. Each AI initiative is catalogued with its risk classification (per EU AI Act tiers), data lineage, responsible team, and deployment status.

Automated compliance checks run continuously, flagging initiatives that lack required documentation, use prohibited data sources, or have not completed mandatory impact assessments. Governance workflows ensure that high-risk AI systems go through appropriate review gates before deployment.

The result is a single source of truth for AI governance that satisfies regulators, reassures stakeholders, and — most importantly — does not slow down the teams building AI products. Good governance should be a guardrail, not a roadblock.