You're not waiting for enforcement to start. Smart. The EU AI Act and its high-risk provisions push organizations toward governance maturity, which is essential for success. If you treat this as just a compliance task, you're missing the point. Teams that build robust AI governance now will deploy faster, fail less, and earn more stakeholder trust while competitors scramble to document what they built months ago.
This checklist isn't about ticking boxes for an auditor. It's about building the operational discipline that lets you confidently say, "Yes, we can deploy that."
What This Checklist Covers
This is your readiness assessment for high-risk AI systems under the EU AI Act. You're checking if your organization can meet the technical documentation (Annex IV), risk management, data governance, and human oversight requirements before committing to a high-risk deployment. If you can't answer "done" to most of these items, you're not ready to put that system into production in the EU market.
The checklist assumes you've confirmed your system falls under Annex III (employment tools, critical infrastructure, law enforcement, etc.). If you're still debating whether you're in scope, stop here and get that classification locked down first.
Prerequisites
Before you start:
- Risk tier classification completed. You've documented why this system qualifies as high-risk under Annex III.
- Cross-functional team identified. You need legal, compliance, data science, engineering, and business owners in the same room. The EU AI Act requires collaboration.
- Technical Documentation template ready. Annex IV lists 16 required sections. You can't retrofit documentation after the fact.
Checklist Items
1. Risk Management System Established (Article 9)
Have you implemented a continuous risk management process that identifies, analyzes, estimates, and mitigates risks throughout the AI system lifecycle?
Done looks like: A documented process that covers foreseeable misuse, runs before initial deployment and after substantial modifications, and produces dated risk registers mapping risks to specific mitigations. You're applying ISO 31000 principles.
2. Training Data Governance Documented (Article 10)
Can you demonstrate that your training, validation, and test datasets meet relevance, representativeness, accuracy, and completeness requirements?
Done looks like: Data lineage documentation showing provenance, collection methodology, and demographic coverage. You've assessed annotation quality, documented known gaps or biases, and explained how your dataset composition aligns with your intended deployment context. If you used synthetic data or data augmentation, that's documented too.
3. Technical Documentation Prepared (Annex IV)
Have you completed all 16 sections of the Technical Documentation requirements, including system description, development process, monitoring procedures, and risk management outputs?
Done looks like: A living document that an external auditor could read to understand how your system works, what data it uses, what risks you identified, and how you're controlling them. This includes architecture diagrams, data flow maps, model cards, validation results, and mitigation controls.
4. Record-Keeping Infrastructure Deployed (Article 12)
Does your system automatically log events sufficient to enable post-market monitoring and incident investigation?
Done looks like: Automated logging of inputs, outputs, timestamps, and system decisions. You've defined retention periods aligned with the system's risk profile. You can reconstruct a decision made six months ago without relying on someone's memory. For biometric systems or high-stakes decisions, you're logging at the individual transaction level.
5. Transparency Requirements Met (Article 13)
Have you prepared user-facing documentation that explains system capabilities, limitations, accuracy metrics, and human oversight requirements in terms a non-technical deployer can understand?
Done looks like: Instructions for use that don't require a PhD to parse. You've stated accuracy metrics (and their confidence intervals), explained what the system can't do, described appropriate use contexts, and clarified what human review is required.
6. Human Oversight Mechanisms Implemented (Article 14)
Have you designed and tested controls that enable humans to understand outputs, intervene in real-time, and override decisions?
Done looks like: Interface design that surfaces uncertainty, flags edge cases, and makes intervention easy. You've defined who has override authority, trained them on when to use it, and logged override events.
7. Accuracy and Robustness Testing Completed (Article 15)
Have you validated system performance across your intended operating conditions and tested resilience to input perturbations, adversarial examples, and data drift?
Done looks like: Validation evidence showing performance across demographic subgroups, edge cases, and degraded input quality. You've run robustness testing and documented system behavior with out-of-distribution inputs. You know your false positive and false negative rates and have decided they're acceptable given the use case.
8. Cybersecurity Controls Verified (Article 15)
Have you implemented protections against unauthorized access, data poisoning, model extraction, and adversarial attacks?
Done looks like: Threat modeling that considers AI-specific attack vectors. You've applied input validation, rate limiting, and anomaly detection. If you're using third-party models or data, you've assessed AI Supply Chain Compromise risks. Your incident response plan includes model-specific scenarios.
9. Quality Management System Documented (Article 17)
Have you established processes for design review, change control, validation, and post-market monitoring that integrate with your existing quality management framework?
Done looks like: Procedures that define who approves design changes, how you validate updates before deployment, and how monitoring findings trigger reviews. If you're ISO/IEC 42001 certified (or working toward it), your AI Management System already covers this.
10. Conformity Assessment Pathway Identified (Article 43)
Do you know whether you're pursuing internal conformity assessment (Annex VI) or requiring third-party involvement (Annex VII), and have you prepared the required documentation?
Done looks like: A decision on assessment pathway based on your system's characteristics and your organization's quality management maturity. You've identified gaps between what you have and what the chosen pathway requires.
Common Mistakes
Treating this as a documentation exercise. The EU AI Act requires operational controls, not just paperwork. If you can't demonstrate that your risk management process actually changed how you built the system, you've missed the point.
Assuming your existing model validation is sufficient. SR 11-7 validation is necessary but not sufficient. The EU AI Act adds data governance, human oversight, and transparency requirements that most financial services model risk frameworks don't cover.
Delegating to one function. Legal can't write your Technical Documentation. Data science can't design your human oversight controls. Compliance can't validate your model. This work requires collaboration, not handoffs.
Waiting for final guidance. The core requirements are already clear. You can start building risk management processes, improving data governance, and documenting your systems now. Waiting for the AI Office to publish every last FAQ is an excuse, not a strategy.
Next Steps
If you've got more than three "not done" answers, you're not ready for high-risk deployment under the EU AI Act. That's fine. You've identified your gaps.
Your next move: pick the highest-risk item you marked "not done" and assign an owner with a two-week deadline. This isn't a six-month transformation program. It's a series of concrete deliverables that cross-functional teams can complete in sprints.
Organizations that turn compliance into a competitive advantage aren't doing anything magical. They're just starting now instead of waiting for enforcement.
