EU AI Act Compliance Guide and Where EA is the Critical Enabler

AI regulation is no longer a policy issue for legal and compliance teams alone. Organizations need a clear picture of the AI systems they use, the data and applications those systems touch, the business processes they influence, and the people accountable for their outcomes.

That picture is difficult to create when AI is spread across sanctioned platforms, shadow tools, disconnected pilots, and vendor-embedded features. Enterprise architecture provides the connected context leaders need to turn a scattered inventory into an operating model for responsible AI governance.

What you’ll learn in the guide

Inside this concise, visual-first ebook, you’ll discover how to:

• Understand the EU AIAct’s risk-based approach and what it means for organizations that deploy AI.
• Build a governed AI system inventory covering owners, vendors, data sources, integrations, businessfunctions, and risk status.
• Use business and technology context to make more meaningful AI risk-classification decisions.
• Connect policies to decision rights, review paths, controls, escalation routes, and evidence.
• Keep compliance evidence current as AI systems, dependencies, and business priorities change.
• Use enterprise architecture to make AI governance repeatable rather than reactive.

The visibility gap is already creating risk

Orbus research shows that many organizations are moving faster with AI than their governance structures can support. The guide explores what these blind spots mean for leadership, compliance, and enterprise architecture teams.

• 98% of CIOs lack visibility into the technical and business risks of AI
• 78% of CIOs struggle with shadow AI
• 82% rely on enterprise architects to assess AI-related risk
• 80% say manual oversight cannot keep pace

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