EU AI Act handbook: what should governance teams prepare for?

TL;DR: The EU AI Act’s scope, timelines, penalties, risk classes, GPAI obligations, and AI literacy requirement are outlined in Saidot’s AI Act Handbook, with practical preparation steps for organisations facing the regulation’s first in-force obligations. The core governance challenge is moving from policy awareness to inventory, classification, and audit-ready controls before enforcement deadlines compress remediation time.

Editorial analysis by NHI Mgmt Group, based on content published by Saidot: “AI Act Handbook: The essentials of the EU AI Act”.

Key questions

Q: How should security teams structure EU AI Act compliance for AI systems?

A: Start with a complete AI inventory, then classify each system by risk tier and map the required controls to that tier.

Q: Why do AI systems with weak inventory and impact assessments create more governance risk for organisations?

A: AI governance fails when teams cannot see what tools are in use, what data they process, or how decisions affect people.

Q: What breaks when AI literacy training is separated from the workflow?

A: The organisation loses evidence that guidance was received at the moment risk occurred.

Practitioner guidance

• Define the AI system inventory Create a living register of models, applications, and use cases, then assign owners, risk classes, and deployment context to each record.
• Map obligations by role Separate provider, deployer, and GPAI responsibilities so legal, security, product, and engineering teams know which controls they own.
• Build evidence for audit readiness Capture approval records, monitoring outputs, change logs, and incident handling artefacts so compliance can be demonstrated, not just claimed.

Bottom line: The AI Act turns AI governance into a classification, ownership, and evidence problem rather than a simple policy exercise.

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