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How to monitor high-risk AI systems under the EU AI Act
Monitoring, in a serious sense, is not a dashboard of charts nobody owns. It is a disciplined way to know what a live system is doing, how that compares to the limits you set, and what you do when something moves. For teams tracking an in-scope system, that is the only kind of "readiness" that holds up in a review.
Start from one in-scope system
You cannot monitor "AI at the company" in a way that helps you in a real incident. You pick the workflow, the model, and the business output you are responsible for, and you build the record from there. That is the same posture Guardian takes: one shared record per priority system first, then scale when the pattern works.
What to track in production
In practice, teams need: scheduled or event-driven inputs that reflect model and data health; clear thresholds for when a result is not business-as-usual; a path from threshold breach to human review; and a log of what you decided and who signed off. None of that requires a single compliance score. It requires a line of record that shows what you knew and when. That is what product-led monitoring in Guardian is designed to hold together.
How this links to the EU AI Act in day-to-day work
The Act asks serious teams to run high-risk systems with risk management, technical documentation, and post-market monitoring that they can show—not only once at launch. The operational part is: can you show how the system behaved in life, and what you did about it? This article is about that second half. The legal labelling of your use case is a different conversation; the engineering and governance work still has to run to the same standard if you are on the hook for a production system.