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.
To prepare a high-risk AI system for conformity under the upcoming EU AI Act, our most concrete step has been implementing a technical documentation pipeline integrated with a model risk register, backed by legal audit checkpoints. We developed a modular documentation pipeline that begins at the design phase and extends through post-deployment. Every stage—data sourcing, model selection, training, testing, explainability, and user interaction—feeds into a structured compliance dashboard. This allows legal, compliance, and technical teams to collaborate on versioned entries, ensuring traceability and audit readiness. The most effective tool in practice has been our model risk register, where each risk entry is linked to mitigations, stakeholder feedback, and corresponding documentation artifacts (e.g., bias test results, red-teaming reports, human oversight logs). We aligned it with the EU AI Act's Annex IV requirements and Article 9 risk management framework. For example, during a dry-run audit of a biometric recognition tool, the model risk register helped us flag and decommission a training dataset with unresolved consent provenance. Having it linked to the technical file meant we could update risk entries and mitigation steps in real-time, satisfying both documentation and risk governance obligations. Additionally, we embedded a post-market monitoring plan focused on human-AI interaction anomalies. This included: mandatory feedback capture from end-users, incident response escalation tied to severity scoring, and quarterly updates to the conformity file. Our legal team ensures that all records meet retention obligations and can be exported for external audits or notified bodies. This hybrid approach—legal + technical—has proven more resilient than relying solely on engineering documentation or external assessments.
Being the Founder and Managing Consultant at spectup, one concrete step we take when preparing a high-risk system for EU AI Act conformity is building a centralized model risk register from day one. Early in my experience, I noticed that teams often scatter risk tracking across documents, spreadsheets, and Slack threads, which makes compliance reviews painfully slow. By consolidating every model, its intended use, risk classification, validation results, and mitigation measures in one living register, we create a single source of truth that aligns technical, legal, and product teams. One example comes from a fintech client deploying a credit scoring AI. During week one of compliance prep, we logged each model version, its performance metrics, bias testing outcomes, and deployment context. This allowed us to run a structured conformity gap assessment and immediately flag high-risk features. One of our team members pointed out that a scoring sub-model had drifted slightly from validated thresholds, which could have caused regulatory exposure if left unchecked. Because the register was live and structured, we corrected the drift within days rather than weeks. The model risk register also feeds into other requirements under the AI Act, like post-market monitoring and technical documentation. It ensures that updates, retraining, or new data sources are logged and reviewed systematically. At spectup, we integrate this with automated reporting so that every version update generates a compliance snapshot without manual compilation. What makes this step most effective is visibility and traceability. Auditors, executives, and product owners can instantly see risk status and mitigation plans, which accelerates both internal decision-making and regulatory confidence. In my experience, without a living register, teams waste weeks reconciling ad hoc notes, and small oversights compound into major delays. This approach has proven to turn compliance from a bottleneck into a repeatable, auditable process, giving both founders and regulators clear insight into risk management at scale.
One concrete step we're taking is standing up a living technical documentation pipeline that auto-updates model cards, data provenance, risk controls, and evaluation results on every release. The most effective piece has been a model risk register wired to CI/CD, where each change requires explicit sign-off on intended use, performance deltas, bias checks, and mitigations before deploy. This matters because conformity assessment fails when docs drift from reality. In practice, the pipeline caught a training data refresh that altered class balance and would have invalidated prior metrics, letting us re-run tests and update documentation immediately instead of retrofitting weeks later Albert Richer, Founder, WhatAreTheBest.com
For EU AI Act prep, the most effective step has been standing up a live model risk register tied to deployment changes. Every update triggers documentation and review automatically. That keeps technical files current and audit ready. It also forces clearer ownership. Compliance stays continuous instead of reactive.
I appreciate the question, but I need to be transparent here: at Fulfill.com, we're not currently developing high-risk AI systems that would fall under the EU AI Act's conformity assessment requirements for 2026. Our AI applications in logistics and fulfillment optimization don't meet the high-risk classification thresholds defined in the Act. That said, I've been closely monitoring the EU AI Act because it's setting the global standard for responsible AI deployment, much like GDPR did for data privacy. Even though our systems aren't classified as high-risk, we're proactively implementing documentation practices that align with the Act's principles. The most valuable step we're taking is building what I call a "decision audit trail" for our AI-powered warehouse matching and inventory forecasting systems. Every time our algorithms make a recommendation, whether it's suggesting a 3PL partner for a brand or predicting inventory needs, we're documenting the input data, the decision logic, and the outcome. This creates a transparent record that we can review and validate. From my experience working with hundreds of e-commerce brands through our platform, I've learned that documentation isn't just about compliance. It's about building trust and improving performance. When we can show brands exactly why our system recommended a particular warehouse in Ohio over one in Pennsylvania, citing factors like shipping zones, storage costs, and delivery speed, they trust the technology more and make better decisions. If I were advising companies with high-risk AI systems, I'd emphasize starting with a robust model risk register immediately. Don't wait until 2025. The register should track every AI model, its intended use, potential failure modes, and mitigation strategies. In logistics, where AI decisions affect real supply chains and real customer deliveries, understanding failure scenarios isn't theoretical, it's operational necessity. The EU AI Act is pushing the entire industry toward transparency and accountability, which ultimately benefits everyone. Even if your systems aren't high-risk today, building these practices now means you're prepared for regulatory evolution and customer expectations tomorrow.