EU AI Act AI evaluation evidence: what to collect before a high-risk system goes live | Galtea Blog

For teams developing or deploying AI systems that may qualify as high risk under the EU AI Act, audit-ready evidence should connect every release to its intended purpose, system design, risk and data decisions, repeatable evaluations, logs and traces, human oversight, incidents, corrective actions, and release approvals. The guidance distinguishes technical documentation and record-keeping requirements under Articles 11 and 12 from broader obligations on risk management, data governance, human oversight, accuracy, robustness, cybersecurity, quality management, and corrective action under other provisions. It recommends maintaining versioned, exportable records that document evaluation scenarios, metrics, thresholds, configurations, results, reviewer decisions, and subsequent remediation rather than relying on isolated test scores or screenshots. A proposed evidence chain links requirements to test scenarios, pass conditions, named system versions, human review, release decisions, and post-release monitoring, illustrated through a banking assistant example. The article emphasizes that tests, traces, and evaluation platforms can support compliance evidence but do not themselves establish legal compliance or replace legal advice, technical documentation, quality systems, or required conformity assessments.

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