EU AI Act vs GDPR: Practical Differences for AI Teams

Different legal nature.

The EU AI Act is a product safety regulation. It treats AI systems as products that must meet safety, transparency, and governance standards before being placed on the market. The GDPR is a data protection regulation. It treats personal data as a fundamental rights issue and regulates how organizations collect, use, store, and transfer it. The AI Act is closer to the Medical Devices Regulation or the Machinery Directive in spirit; the GDPR is closer to the ePrivacy Directive in its focus on individual rights.

Different scope triggers.

The AI Act is triggered by the AI system itself and its output. If an AI system produces results that are used in the EU, the Act applies, regardless of whether personal data is involved. The GDPR is triggered by the processing of personal data. If an AI system processes no personal data, the GDPR does not apply. Conversely, a non-AI system that processes personal data falls under the GDPR but not the AI Act.

Different risk frameworks.

The AI Act classifies AI systems by risk tier: prohibited, high-risk, limited-risk, and minimal-risk. The GDPR does not classify systems by risk in the same way, though it does require data protection impact assessments (DPIAs) for high-risk processing under Article 35. The AI Act's risk classification is product-centric; the GDPR's is data-centric.

Different documentation requirements.

The AI Act requires technical documentation (Article 11), a risk management system (Article 9), and record-keeping (Article 12). The GDPR requires records of processing (Article 30), privacy notices (Articles 12-14), and data protection impact assessments (Article 35). The content, format, and audience for these documents differ. Technical documentation is designed for market surveillance authorities; records of processing are designed for data protection authorities.

Different enforcement bodies.

The AI Act is enforced by market surveillance authorities in each Member State, coordinated at the EU level by the AI Office for general-purpose AI models. The GDPR is enforced by national data protection authorities, coordinated by the European Data Protection Board. A US company may face simultaneous or sequential investigations by different authorities for the same AI system.

Different penalty structures.

The AI Act penalties are defined in Article 99 and are structured by violation type: up to €35 million or 7% of turnover for prohibited practices; up to €15 million or 3% for high-risk non-compliance; and up to €7.5 million or 1% for information violations. GDPR penalties are defined in Article 83 and are structured by infringement type: up to €20 million or 4% of global turnover for fundamental principle violations; and up to €10 million or 2% for other violations. The maximum percentage is higher under the GDPR (4% vs. 7% for Tier 1 AI Act, but 3% for the more common Tier 2).

Different transparency requirements.

The AI Act requires transparency for limited-risk systems (Article 50) and detailed transparency for high-risk systems (Articles 13 and 50). The GDPR requires transparency about personal data processing (Articles 12-14). An AI chatbot must disclose that it is AI under the AI Act and must disclose what personal data it collects under the GDPR. These disclosures may be combined in practice but have different legal foundations.

Overlap in bias and fairness.

Both frameworks address algorithmic bias. The AI Act requires data governance (Article 10) and bias mitigation for high-risk systems. The GDPR requires fairness in automated decision-making (Article 22) and prohibits discrimination under Article 5(1)(b). The Digital Omnibus created a specific legal basis for bias detection under the GDPR, but the underlying obligations remain distinct.

Do not assume that GDPR compliance satisfies AI Act requirements, or that AI Act compliance satisfies GDPR requirements. Establish a cross-functional working group that includes legal, data protection, engineering, and product stakeholders. Map each obligation to the responsible function.

2. Map overlapping obligations.

Create a matrix that identifies where the AI Act and GDPR impose similar but distinct requirements. For example, both require transparency, but the AI Act's transparency is about system nature and capabilities, while the GDPR's transparency is about data use. Both require risk assessment, but the AI Act's is product-oriented and the GDPR's is data-oriented.

3. Align documentation.

Where possible, align technical documentation under Article 11 with records of processing under Article 30 GDPR. This does not mean merging the documents; it means ensuring that common elements (data sources, processing purposes, system logic) are consistent across both. Inconsistencies between AI Act and GDPR documentation are a red flag for regulators.

4. Plan for dual enforcement.

A US company operating an AI system that processes personal data may face enforcement from both market surveillance authorities (AI Act) and data protection authorities (GDPR). Ensure that response protocols, legal counsel, and documentation are prepared for both tracks. The authorities may share information, but their investigative processes are independent.

5. Use the new bias detection legal basis.

The Digital Omnibus created a specific legal basis for processing special categories of personal data under Article 9 GDPR for bias detection purposes. If your AI Act risk management system requires bias testing on sensitive attributes, document the legal basis carefully and implement the required safeguards. Do not rely on vague consent or legitimate interest arguments.

6. Train teams on both frameworks.

AI literacy under Article 4 of the AI Act and data protection training under GDPR accountability principles should be complementary. Engineers should understand when they are building an AI system (AI Act) and when they are processing personal data (GDPR). Product managers should understand both the risk classification and the data protection impact.