Skip to main content

EU AI Act Compliance

The EU Artificial Intelligence Act is the world's first comprehensive legal framework for artificial intelligence. It classifies AI systems by risk level — from unacceptable to minimal — and imposes requirements for transparency, documentation, human oversight, and data quality that scale with risk. Qarion's governance platform helps organizations manage these obligations from the data layer up, where much of AI compliance begins.

AI System Documentation and Registry​

The EU AI Act (Articles 11, 13, 53) requires providers and deployers of high-risk AI systems to maintain technical documentation and register systems in the EU database. Qarion supports this through:

  • Use Case Management — AI projects can be documented as structured Use Cases within the platform, capturing their purpose, scope, risk classification, data sources, and processing activities.
  • Data Catalog as an AI inventory — Each AI system's training data, validation datasets, and output pipelines can be cataloged as data products with rich metadata, ownership, and classification.
  • Change request workflows — Modifications to AI systems trigger formal change requests that route through governance approval workflows, creating a documented record of how AI systems evolve over time.

This structured documentation forms the foundation of the conformity assessments the EU AI Act requires for high-risk systems.

Transparency and Traceability​

Articles 12–14 require that high-risk AI systems are designed to be transparent and that their operations can be traced. Qarion provides:

  • Data lineage graphs — Interactive lineage visualization shows exactly where training data originates, how it is transformed, and which AI models depend on it. This supports the "traceability of results" requirement.
  • Source system registration — Every data source feeding into AI systems is documented in the source system registry with connection details, credential management, and metadata.
  • Audit trails — All data access, transformations, and governance decisions related to AI systems are logged, providing the operational transparency regulators expect.

Training Data Quality​

Article 10 requires that training, validation, and testing datasets meet specific quality criteria including relevance, representativeness, and freedom from errors. Qarion's data quality engine directly supports this:

  • Automated quality checks — Define validation rules for completeness, accuracy, consistency, and other quality dimensions of training datasets. Checks run on schedule or on demand.
  • Quality trend dashboards — Monitor data quality scores over time to detect degradation that could affect model performance or introduce bias.
  • SLA monitoring — Set quality SLAs for training data pipelines and receive alerts when thresholds are breached, preventing poor-quality data from entering AI training workflows.
  • Alerts and annotations — When quality issues are detected, the Smart Alerts Center surfaces them immediately. Annotations provide a mechanism to document investigation findings and remediation actions.

Human Oversight​

Article 14 requires that high-risk AI systems be designed to allow effective human oversight. Qarion supports human-in-the-loop governance through:

  • Approval workflows — Built-in approval actions within the workflow orchestration engine require human review and sign-off at designated points. Approvers are resolved by governance role (e.g., Data Owner, Data Steward), ensuring the right people review the right decisions.
  • Governance meetings — Regularly scheduled meetings with structured agendas, participant tracking, and action items provide a forum for human review of AI system performance and governance.
  • Issue escalation — The issue management system supports structured escalation paths with impact assessment, ensuring that significant AI-related incidents receive appropriate human attention.

Risk Assessment and Conformity​

The EU AI Act's risk-based approach requires ongoing assessment of AI system risks. Qarion supports this through:

  • Use case lifecycle management — AI projects move through documented lifecycle stages, with governance checkpoints at each transition.
  • Risk classification on data products — Every data product (including AI Systems) can be assigned a risk classification level aligned with the EU AI Act taxonomy: unacceptable, high, limited, minimal, or none. Changes to risk classification are tracked in the product's audit history.
  • Impact assessment via lineage — Lineage-based impact analysis helps assess the downstream consequences of changes to AI data pipelines, models, or configurations.
  • Data contracts — Formal agreements between data producers and AI system operators define quality expectations, access terms, and SLAs that support conformity documentation.
  • Comprehensive documentation — The combination of catalog metadata, lineage, quality histories, audit trails, and governance meeting records produces the kind of systematic documentation that conformity assessments demand.