The TimeXtender Data Platform:
Supporting EU AI Act readiness for high-risk AI systems (Articles 10 to 13)
The EU AI Act sets requirements for providers of high-risk AI systems, including how training, validation, and testing data is governed, how technical documentation is maintained, how logging is enabled, and what information must be provided to deployers.
TimeXtender supports these obligations by capturing and activating metadata across data integration, enrichment, quality, and orchestration workflows. This helps teams produce traceable evidence of data origin, transformations, quality controls, and operational execution for the data pipelines that feed high-risk AI systems.
Important note: This document is informational and does not constitute legal advice. Compliance depends on the full AI system design, documentation, and operational controls, not only the data pipeline.
Data Processing & GDPR at TimeXtender
Article 10: Data and Data Governance
High-risk AI systems must be developed using training, validation, and testing datasets that are governed and managed appropriately for the intended purpose, including measures that support data quality, relevance, representativeness, and the identification and mitigation of potential bias sources.
TimeXtender supports Article 10 data governance and management practices by providing technical capabilities that make datasets and transformations traceable, testable, and auditable across the data environment:
Automated Data Lineage and Traceability: TimeXtender documents data movement and transformation steps based on metadata, which supports auditability of dataset origin and preparation steps.
Bias Analysis Enablement: TimeXtender supports bias analysis by making dataset provenance, coverage, transformations, and quality results visible and reviewable, which is often required to run bias assessments and remediation workflows.
Data Quality Controls and Monitoring: TimeXtender Data Quality supports automated profiling, rule-based validation, and continuous monitoring to identify anomalies and data issues before they propagate into downstream analytics or model training datasets.
Governance Support through Access Control and Policy Execution: TimeXtender supports role-based and granular access controls and governance practices that can be applied consistently using metadata-driven configuration.
Article 11: Technical Documentation Requirements
Providers of high-risk AI systems must draw up and keep technical documentation, including information needed to assess compliance and to understand the system’s design and development choices. The AI Act also ties documentation expectations to Annex IV.
TimeXtender supports the technical documentation burden specifically for the data pipeline and data preparation components that feed the AI system:
Automated Documentation for Data Flows and Transformations: TimeXtender can generate documentation from metadata that captures sources, transformations, workflows, and configuration details for the data environment.
Central Metadata Repository: TimeXtender maintains a metadata foundation that records data assets, transformation logic, and dependencies, which can be exported and reused in internal documentation.
Versioning and Change Traceability: TimeXtender supports version-controlled data flows and change history so teams can show what changed, when, and what downstream assets are impacted.
Article 12: Record-Keeping and Logging
Article 12 requires high-risk AI systems to technically allow automatic recording of events (logs) over the lifetime of the system. Logging capabilities must support traceability for risk identification, post-market monitoring, and monitoring of operations.
TimeXtender supports logging and auditability for the data workflows that feed high-risk AI systems:
Execution Logging for Data Workflows: Orchestration and data pipeline execution can produce logs for runs, steps, failures, and dependency behaviour, supporting operational traceability of data processing.
Real-Time Monitoring and Notifications: TimeXtender supports execution monitoring and alerts so teams can respond quickly to failed runs or abnormal behaviour in upstream data preparation.
Audit Trail for Data Processing Activity: TimeXtender supports audit-ready traceability of data origins and transformations through lineage and documentation outputs.
Important Scope Clarification: Article 12 is about the high-risk AI system’s logging capability. TimeXtender logs cover the data pipeline and workflow execution. They do not replace application-level logging inside the AI system itself.
Article 13: Transparency and Information to Deployers
High-risk AI systems must be designed so that their operation is sufficiently transparent, enabling deployers to interpret outputs and use the system appropriately. They must also be accompanied by instructions for use that include concise, complete, correct, and clear information relevant and comprehensible to deployers.
TimeXtender supports the data-related portion of Article 13 by helping providers produce clear, auditable information about input data and data preparation:
Clear Data Documentation for Deployer-Facing Materials: TimeXtender can generate documentation for data sources, transformations, and quality checks to explain input data specifications and data preparation steps.
Lineage Visibility for Interpretability of Upstream Data: Lineage outputs help teams explain where input data came from, what changed, and what downstream artefacts depend on it, which improves transparency for deployment and operational teams.
Business-Friendly and AI-Ready Terms for Data Assets: TimeXtender supports renaming and structuring data assets into business-friendly and AI-ready terms to reduce ambiguity in data handoffs to downstream users and systems.
Important Scope Clarification: Article 13 also covers transparency about the AI system’s characteristics, limitations, and appropriate use. TimeXtender can support the data inputs and data operations documentation, but it does not replace model documentation, human oversight design, or system-level transparency work.
Data Security and Governance
TimeXtender’s architecture is designed to operate on metadata in the control plane. Customer data remains in the customer-controlled source and target systems, and TimeXtender does not store or control the customer’s actual data.
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