Data provenance refers to the full record of the origin, lineage, and transformation of data used in AI systems, from initial collection to preprocessing, labelling, and use in model training and validation. In AI assurance, understanding data provenance is essential to evaluating the quality, fairness, security, and legality of the data that underpins model behaviour.

Assurance of data provenance helps identify risks related to bias, data leakage, intellectual property violations, and regulatory non-compliance. For example, a facial recognition model trained on datasets without sufficient demographic diversity may show performance gaps across populations — a risk that could be identified and mitigated through clear provenance records.

An effective data provenance framework typically includes:

• Metadata describing the original source and context of data collection.

• Documentation of data selection, filtering, and transformation processes.

• Logs of who accessed or modified the data, and when.

• Version control and audit trails for data pipelines.

• Details of licensing, consent, and ethical approvals.

In safety-critical applications — such as surveillance, health diagnostics, or autonomous systems — poor data provenance can undermine model validity and expose organisations to legal or ethical liability. For instance, using outdated or mislabelled sensor data in defence scenarios may lead to real-world operational errors.

AI assurance practices examine data provenance to verify that datasets are:

• Sourced from trustworthy and authorised providers.

• Representative of the deployment context.

• Free from harmful or unintended correlations.

• Handled in compliance with data protection regulations (e.g., GDPR).

Tools such as data lineage graphs, automated metadata capture, and secure data versioning support provenance management. These capabilities also enable reproducibility, facilitate auditing, and enhance transparency for end users and regulators.

Regulatory frameworks increasingly require documentation of data provenance as a condition for compliance. The EU AI Act, for instance, mandates data governance measures and record-keeping obligations for high-risk AI systems. ISO/IEC 5259 and NIST guidelines also emphasise data lineage as a best practice.

By enabling full traceability and accountability of training data, strong data provenance practices form a critical pillar of trustworthy AI assurance. They help ensure that model behaviour is interpretable, predictable, and rooted in high-integrity data foundations.