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How to monitor high-risk AI systems under the EU AI Act
Monitoring high-risk AI systems means maintaining a continuous record of performance, fairness, oversight, incidents, and evidence after deployment — not just documenting the system once.
Guardian is a continuous monitoring and auditability platform for high-risk AI systems under the EU AI Act. See the product, the EU AI Act overview, open methodology, and the 4-week Readiness Sprint. For citation-friendly definitions, use For AI; for data handling, see security.
What monitoring means in practice
For high-risk AI systems, monitoring is not just model telemetry. It is the ongoing record of how the system behaves, what changes over time, what incidents occur, what oversight actions are taken, and what evidence is maintained for later review.
What teams should monitor
- Drift and performance change over time
- Fairness and cohort-level differences
- Data quality issues and anomaly signals
- Human oversight actions and interventions
- Incidents and remediation steps
- Documentation and evidence continuity
Why monitoring often breaks after deployment
Many organisations can describe a system at launch. Fewer can maintain a durable operational record once the system is live.
Signals change. Incidents happen. Oversight actions are taken across multiple teams. Evidence becomes fragmented across tools, documents, and email threads.
That is why post-deployment monitoring is often weaker than pre-deployment documentation.
What a minimum viable monitoring baseline looks like
- One clearly defined in-scope AI system
- A small set of signals that matter operationally
- Defined thresholds or review triggers
- A structured incident and follow-up record
- Human oversight logging
- A maintainable evidence trail for later review
Why one system first is the right starting point
The fastest path to credible AI Act readiness is usually not a broad transformation programme. It is a focused baseline around one real system first.
That is the model Guardian uses in the 4-week Readiness Sprint. Company context: About.
FAQ
- What should teams monitor for a high-risk AI system?
- Teams usually need to monitor drift, fairness, data quality, oversight actions, incidents, and evidence continuity.
- Is monitoring the same as compliance?
- No. Monitoring supports readiness by helping teams maintain a usable operational and evidence record. Legal compliance remains a separate determination.
- Why is post-deployment monitoring so difficult?
- Because signals, incidents, and oversight actions often become fragmented across teams and tools once a system is live.
- Can we start with one system only?
- Yes. Starting with one real system is usually the fastest way to build a credible monitoring baseline.
Start with one high-risk AI system
Book a readiness call to define what should be monitored for one live system and what a first evidence baseline could look like.