What should security teams prioritise first for EU AI Act readiness?

Why inventory and classification should come before control design

EU AI Act readiness starts with knowing exactly which AI systems exist, who owns them, what data they touch, and whether they are genuinely in scope for higher obligations. Without that baseline, teams tend to apply controls unevenly, miss hidden deployments, and overbuild governance around low-risk use cases while leaving material ones poorly managed.

The practical first move is to build a living inventory that records the system purpose, business owner, vendor, model source, deployment context, and the human or team accountable for decisions. An AI Register and AI Inventory Template is useful here because it turns readiness into a traceable register rather than an ad hoc spreadsheet.

For teams handling agentic or semi-autonomous systems, the same inventory should also capture the agent ownership, autonomy and risk tiers that change how oversight and approval need to work.

How risk classification shapes the first governance decisions

Once systems are inventoried, the next priority is classification. That means deciding which AI uses are low risk, which are materially governed by internal policy, and which may fall into higher-impact categories that need stronger documentation, testing, human oversight, and formal sign-off. The value of classification is not academic, it determines where security teams need to focus first.

That classification should also drive a simple question: what evidence will you need if someone later asks why a system was approved, by whom, and under what safeguards? The EU AI Act Compliance Guide helps teams anchor those decisions to the regulation’s roles, risk categories and obligations rather than to a generic security checklist.

Where a use case may be high risk, the stronger first step is to treat the classification outcome as a control trigger, not a label. That is the point at which security, legal, privacy, data governance and model owners need to agree who is accountable for ongoing review, change approval, and exceptions.

What to lock down immediately after inventory and classification

After the inventory and risk rating are in place, security teams should prioritise the controls that reduce blast radius and create evidence. That usually means access logging, model change control, supplier review, and restrictions on sensitive data in prompts, training sets and embedded documentation. These controls are most effective when applied to the systems that are most exposed, not to every AI use case equally.

For organisations still building operating discipline, the AI Security Maturity Model is a helpful way to sequence readiness work from inventory to more advanced governance and control depth. It is also a reminder that readiness is progressive, so the first version of the programme should favour visibility, ownership and approval gates over perfect completeness.

Where vendors provide the model or platform, supplier due diligence should ask who can change the model, what is logged, how incidents are reported, and whether data handling matches the intended use. That is especially important when the same system is embedded across business units or shared through common platforms, because one weak dependency can affect many downstream processes.

Risk and Threat Considerations

The main risk is starting with policy wording instead of system discovery. If teams cannot see all AI systems, they cannot classify them accurately, and they will miss the places where model drift, unapproved change, vendor exposure, or sensitive data misuse creates real operational and compliance risk.

Failure mechanism: Shadow deployments, weak ownership, and incomplete system records prevent teams from knowing which AI use cases need stronger governance, so controls are applied too late or to the wrong systems.

Impact: Misclassified or unseen systems can create uncontrolled data exposure, weak audit evidence, inconsistent approvals, and gaps in accountability when an AI decision or output is challenged.

Practitioner Guidance

What to prioritise: Start with a minimal but defensible AI register that captures owner, vendor, model, data sources, user group and intended use. If a system cannot be assigned to a named owner, it is not ready for broader rollout.

Decision rule: If the system can process sensitive, regulated or high-value business data, treat prompt handling, logging and change control as baseline requirements before expanding usage. If it cannot, keep the governance lighter, but do not skip inventory or ownership.

What good looks like: Security teams can answer, for every material AI use case, who owns it, what it does, what data it uses, where it runs, who can change it, and what evidence exists for approval and review.

Practitioner takeaway: EU AI Act readiness is won by visibility first, because classification and control design only work once the organisation can reliably name every AI system, its owner and its exposure.

Related resources from NHI Mgmt Group

• Guide: Agentic AI Compliance Guide
• Why does the EU AI Act force organisations to prioritise high-risk AI systems first?
• How should security teams structure EU AI Act compliance for AI systems?
• What should teams prioritise first when aligning AI RMF with existing security programmes?
• How should security teams build AI systems to meet EU AI Act requirements without slowing delivery too much?