EU AI Act Readiness: One Step That Works
Organizations rushing to comply with the EU AI Act face a complex challenge: tracking and managing AI systems across their entire operation. Industry experts agree that maintaining a continuous model risk registry stands out as the most effective starting point for compliance. This single step provides the foundation needed to meet regulatory requirements while building a sustainable AI governance framework.
Adopt a Continuous Model Risk Registry
We're implementing the EU AI Act's requirement of a continuous risk management system by ensuring that our model risk register is a living, auditable system rather than a static document. We treat it as the single source of truth for the system's intended purpose, for data governance, known limitation, and for testing results against foreseeable misuse. We find this works so much better as it drives a cross-functional review of the harms we expect and how we think we can mitigate them *before* any of the technical docs are written, meaning the resulting documentation is more honest to our risk management rather than an afterthought.
Build a Searchable AI System Inventory
Keeping a full inventory of all AI systems creates a single source of truth. It shows what each system does, who owns it, where it runs, and what data it uses. This helps map EU AI Act duties and makes audits faster because records are ready. A living inventory also reduces shadow AI by making teams register new tools early.
Tie the registry to intake, change, and retire steps so updates happen by default. Add checks for accuracy and schedule reviews so gaps are found quickly. Start a shared and searchable AI registry now.
Appoint an Accountable Compliance Owner
Naming one accountable AI compliance owner removes confusion and gaps. This role sets policy, tracks metrics, and coordinates legal, risk, data, and product teams. Clear authority and budget let the owner say no when controls are weak. A single point of contact also helps answer regulator and customer questions fast.
The owner can build common templates and training so every team moves in sync. Define the role in the org chart and back it with the right mandate. Appoint a responsible owner now.
Classify Projects Before Code Starts
Early risk classification tells teams what rules will apply before they write code. It separates high risk uses from low risk ideas and stops banned use cases at the gate. Clear labels guide the right level of testing, data controls, and human oversight. This avoids late rework and speeds funding choices with facts.
It also helps vendors and partners plan their part of the controls. Use a simple intake form and review board to rate risk at the start of each project. Make risk tagging a design step today.
Strengthen Supplier Contracts With Tech Clauses
Strong supplier contracts make AI duties travel across the chain. Clauses can require model transparency, data source checks, and timely incident reports. They can allow audits, set security and logging standards, and define retraining triggers. Clear terms on rights, limits, and support prevent finger pointing when problems arise.
Flow-down language also covers sub-processors so there are no weak links. Standard terms reduce work for each deal and lower legal risk. Update key vendor contracts with AI clauses now.
Conduct a Fundamental Rights Assessment
A Fundamental Rights Impact Assessment brings human rights into AI design. It checks effects on privacy, fairness, access, and freedom, using plain questions. Input from affected users and staff helps find harm that code scans miss. The process records risks, options, and safeguards, and shows why choices were made.
This proof supports EU AI Act duties and builds trust with customers and staff. Doing it before launch costs less than fixing harm in the field. Start a FRIA on your next AI change before any release.