Breaches happen. Models drift. Pipelines grow complex. In the middle of all that, the EU AI Act raises the bar on how organizations collect, govern, and secure training, validation, and testing data—especially when it includes personal data (PII). The Act is already rolling out on a fixed timetable, with general-purpose AI (GPAI) transparency duties starting in August 2025 and the bulk of high-risk obligations applying from August 2026, with full effect reached by 2027. Reuters

This article summarizes what the law expects from your datasets, how this interacts with GDPR, and how a data-first security layer like Privicore helps you keep sensitive features protected, so even if artifacts are exfiltrated, the data has zero value to attackers.

• Law in force: The AI Act was published in the EU Official Journal on 12 July 2024, and entered into force shortly after.

• Early measures live: Initial bans on certain unacceptable AI practices began taking effect in early 2025 across the EU.

• GPAI transparency: Obligations for providers of general-purpose AI models begin August 2025 (e.g., transparency about training data and technical documentation).

• High-risk systems: Core requirements, including data and data-governance duties, apply broadly from August 2026; the framework completed by 2027. European Parliament

For high-risk AI systems, Article 10 sets out concrete duties for the data used at training, validation, and testing stages. In plain terms, your datasets must be: high-quality and relevant, properly governed, documented for provenance, and monitored for bias. You need traceability over sources, labeling/cleaning steps, and known limitations.

At a glance, Article 10 means you should be able to show:

• Quality & relevance: The data fits the purpose, with known caveats recorded.

• Governance: You document sourcing, labeling, cleaning, representativeness, and limitations.

• Provenance & traceability: You can track where records came from and how they changed.

• Bias management: You detect, test for, and mitigate discriminatory outcomes.

Tip: The official text (Regulation (EU) 2024/1689) allows using certified third-party services to help verify dataset governance and integrity, useful when you need external assurance for audits. EUR-Lex

The AI Act doesn’t replace GDPR. If your model touches personal data, you still need a lawful basis, data minimization, purpose limitation, and robust anonymization/pseudonymization, plus a way to honor rights requests (access, deletion where applicable).

In practice:

• Don’t train on raw PII unless it’s essential and lawfully justified.

• Prefer pseudonymized or tokenized values for features that don’t need raw identifiers.

• Be transparent: keep notices current and be ready to respond to DSARs that touch training data.

Most AI teams already version datasets and models. The gaps usually appear around field-level protection, provenance across many tools, and controlled de-tokenization at compute time. That’s where a data-first security layer helps.

A practical, compliant AI data pipeline

Ingest → Lake/Lakehouse → Feature Store → Training/Validation → Deployment/Inference → Monitoring

1. Classify PII at ingest
   Automatically tag sensitive columns (emails, phones, payment data, national IDs, health fields). Keep a clear catalog of what’s personal data and what’s not. (Supports Article 10 and GDPR data minimization.)

2. Apply field-level protection early
   Use tokenization or format-preserving encryption (FPE) on sensitive attributes before they land in the feature store. Preserve format and statistical properties where needed so the feature remains useful. (Limits breach impact and supports privacy-by-design.)

3. Provenance & lineage by default
   Log data sources, versions, schema changes, and transformations. Record which features were protected and how; link each model version back to the protected training slice. (This maps directly to Article 10’s provenance/quality asks.)

4. Role-based, least-privilege access
   Data scientists see what they need; raw PII access is the exception, not the rule. Enforce policy-gated de-tokenization only for approved jobs and only for the minimum fields required.

5. Retention & deletion
   Time-box sensitive datasets and training snapshots. Build delete workflows that touch both the feature store and any backup or checkpoint locations (helps with GDPR storage limitation).

6. Breach posture: remove the payoff
   Assume a bad day: if a feature store or checkpoint leaks, protected fields should be unusable without the separate key/token vault. That turns exfiltration into noise instead of a crisis.

If you provide or significantly fine-tune GPAI/foundation models, the AI Act adds transparency and technical documentation duties, starting August 2025. You’ll need to summarize training data sources and maintain more detailed system documentation; the largest models also face additional cybersecurity risk measures. Plan time for documenting sources and producing readable summaries for auditors and downstream customers.

Privicore is a drop-in security layer for developers that protects sensitive fields at the data layer, end-to-end, without breaking model quality.

• At ingest: Auto-detect PII → apply tokenization/FPE to sensitive columns before they enter your lake or feature store.

• In the feature store: Keep protected values as the default; preserve consistency across datasets so the modeling signal remains intact.

• At training time: Policy-gated de-tokenization for specific features/jobs, with full audit trails for who, what, when, and why.

• At inference: Real-time checks control which services can see raw values; defaults favor masked/protected outputs.

• For compliance: Exportable lineage and protection status help you evidence Article 10 practices and prepare training-data summaries for GPAI obligations.

Outcome: even if an attacker steals a training snapshot, feature table, or model artifact, the sensitive values are worthless without keys from your separate vault.

Security at the perimeter is necessary, but it’s no longer sufficient. The AI Act forces teams to show exactly how they govern and secure their training data. That’s good pressure. With a data-first approach, you reduce risk and keep moving fast.

Privicore – The Data First Defender.
Data stays safe, even in breaches.

👉 Learn more about how Privicore keeps your data safe: privicore.com