如何遵守欧盟人工智能法案的数据透明度要求

As of August 2, 2026, the European Union has transitioned from the initial grace period of the AI Act to active enforcement of its most stringent transparency and high-risk system obligations. For organizations sitting on vast proprietary datasets and the AI teams seeking to acquire them, the regulatory landscape has shifted from voluntary ethical guidelines to a mandatory legal framework with significant financial consequences. Non-compliance can now result in administrative fines of up to €35 million or 7% of total global annual turnover for the preceding financial year, whichever is higher (European Commission).

This enforcement milestone specifically targets the transparency of General-Purpose AI (GPAI) models and the rigorous data governance required for "high-risk" AI systems. For the data-asset market, this means that the value of a dataset is no longer determined solely by its volume or uniqueness, but by the auditability of its provenance. To acquire rare compliant training data EU AI Act standards now dictate, buyers must demand granular documentation that was previously considered optional.

The Data Governance Standard for High-Risk Systems

Under Article 10 of the AI Act, high-risk AI systems that make use of techniques involving the training of models with data must be developed on the basis of training, validation, and testing data sets that meet specific quality criteria. Data owners must now be prepared to provide evidence of:

• Design Choices: Documentation of the initial data collection processes and the rationale behind specific data sources.
• Data Provenance: A clear chain of custody that tracks how data was acquired, including licensing rights and original authorship.
• Bias Mitigation: Evidence that the data sets are representative and that potential biases (e.g., gender, race, age) have been identified and addressed through technical measures.
• Data Cleaning: Detailed logs of how data was filtered, transformed, or augmented before being used for training.

Transparency Requirements for General-Purpose AI (GPAI)

As of the August 2026 deadline, providers of GPAI models must comply with specific transparency obligations regardless of whether the model is integrated into a high-risk system. According to the official regulatory framework, providers must draw up and keep up-to-date technical documentation, including the training and testing processes and the results of their evaluation (Cooley).

Crucially for content owners, GPAI providers are now required to publish a "sufficiently detailed summary" of the content used for training the model. This summary is intended to facilitate the exercise of rights by copyright holders who have opted out of text and data mining. For data buyers, this means that acquiring unlicensed or "scraped" data without a clear legal basis now carries a high risk of public exposure and subsequent litigation.

The Financial Stakes: Disclosed Fine Structures

The EU AI Act employs a tiered fine structure to ensure proportionality, but the ceilings are designed to be deterrent. According to the European Commission's digital strategy portal (Source), the following maximum penalties apply:

• €35 million or 7%: For non-compliance with prohibited AI practices.
• €15 million or 3%: For non-compliance with any other requirements of the Act, including data governance and transparency rules.
• €7.5 million or 1.5%: For the supply of incorrect, incomplete, or misleading information to notified bodies or national competent authorities.

For SMEs and startups, these fines are capped at the lower of the two amounts, but for global enterprises, the percentage-based turnover fine represents a catastrophic balance-sheet risk.

Due Diligence Checklist for Data Deals

Whether you are listing assets in our dataset catalogue or evaluating a new acquisition, your due diligence process must now include a "Compliance File." Decision-makers should verify three pillars:

1. Rights Clearance: Does the data owner have the explicit right to sublicense this data for AI training? (Check for TDM opt-outs).
2. Technical Metadata: Does the dataset include metadata describing the cleaning and labeling protocols used?
3. Regulatory Mapping: Has the dataset been audited against the specific requirements of Article 10 if intended for high-risk use cases (e.g., recruitment, credit scoring, law enforcement)?

What this means for you

For Data Owners, the August 2026 enforcement creates a premium for "Clean Data." Datasets with verifiable provenance and bias audits will command higher licensing fees as buyers seek to de-risk their AI deployments. For Data Buyers, the era of "move fast and break things" with training data is over. Compliance is now a prerequisite for market entry in the EU. By utilizing d-nvest's intelligence and marketplace tools, you can ensure that every data transaction aligns with these new transparency mandates, protecting your investment from regulatory clawbacks and reputational damage.