AI inherits every data governance failure at scale. VDF AI Compliance links datasets to registered AI systems and surfaces quality, lineage, and erasure risks before models reach production.
EU AI Act Article 10 requires training data that is relevant, representative, and free of errors. GDPR Article 17 collides with model weights — personal data in parameters cannot simply be deleted. Most companies have governance on paper, not in practice.
Connect enterprise data sources, discover datasets linked to registered AI systems, profile quality and lineage, identify Critical Data Element candidates, and flag GDPR Article 17 risks where personal data lacks an erasure mechanism.
Catalogs datasets connected to registered AI systems across enterprise sources.
Assesses completeness, consistency, duplication, and representativeness.
Traces data from source systems through to model training.
Prioritizes remediation by AI system risk tier with CDE and erasure flags.
Covers EU AI Act Art. 10, GDPR Art. 17, EU Data Act obligations, and ISO 42001 Clause 8.4 with versioned lineage snapshots for auditors.
A practical view of where this workflow breaks, how VDF AI handles it, and what the governed agent stack looks like in production.
AI inherits every data governance failure at scale. VDF AI Compliance links datasets to registered AI systems and surfaces quality, lineage, and erasure risks before models reach production.
EU AI Act Article 10 requires training data that is relevant, representative, and free of errors. GDPR Article 17 collides with model weights — personal data in parameters cannot simply be deleted. Most companies have governance on paper, not in practice.
Connect enterprise data sources, discover datasets linked to registered AI systems, profile quality and lineage, identify Critical Data Element candidates, and flag GDPR Article 17 risks where personal data lacks an erasure mechanism.
Covers EU AI Act Art. 10, GDPR Art. 17, EU Data Act obligations, and ISO 42001 Clause 8.4 with versioned lineage snapshots for auditors.
The workflow is designed to produce measurable operational gains without losing enterprise control.
Typical integrations include SAP Datasphere, Azure Data Lake, PostgreSQL, Snowflake, BigQuery. VDF AI can connect this workflow to adjacent use cases across the same business domain while keeping data, decisions, and review steps governed.
You cannot classify risk you do not know exists. VDF AI Compliance discovers AI systems across enterprise silos and produces a live, audit-backed AI System Register with risk pre-classification.
Read Use CaseAI bias is the obligation companies understand least and fear most. VDF AI Compliance produces Fairness Audit Reports with severity scores, affected characteristics, and remediation plans.
Read Use CaseNo-code RAG for pharma compliance turns SOPs, GxP guidelines, and internal standards into a cited knowledge assistant. VDF AI Networks helps quality teams get validated answers without building custom retrieval systems.
Read Use CasePractical answers for teams evaluating this workflow across security, operations, and deployment.
Talk to an expertA workflow that connects AI systems to their underlying datasets, assesses data quality and lineage, and flags regulatory risks like GDPR erasure conflicts.
AI amplifies existing data problems — poor quality, missing lineage, and personal data in training sets create compliance exposure that paper policies cannot address.
When personal data is used in model training, the right to erasure may conflict with model weights — this use case identifies those datasets before they become liabilities.
Gap reports rank issues by the risk tier of the AI systems that depend on each dataset.
Describe your workflow and we will help map the right governed agent network for your environment.
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