Compliance Guides

EU AI Act vs GDPR: Key Differences and Overlaps

AI Comply HQ TeamMarch 22, 202613 min read

The EU AI Act and the General Data Protection Regulation (GDPR) are two distinct pieces of EU legislation, but they share substantial common ground. Any organization deploying AI systems that process personal data within the European Union must comply with both frameworks simultaneously. Understanding where these regulations overlap, where they diverge, and how to build a unified compliance approach is essential for avoiding enforcement gaps and duplicated effort.

This guide provides a structured comparison of the two regulations and practical guidance on building a compliance framework that satisfies both.

Scope: What Each Regulation Covers

The GDPR and the EU AI Act regulate different aspects of technology, but their scopes intersect significantly when AI systems process personal data.

The GDPR applies to the processing of personal data by controllers and processors established in the EU, or by organizations outside the EU that offer goods or services to, or monitor the behavior of, individuals within the EU. Its scope is defined by the activity (processing personal data) rather than the technology used.

EU AI Act Scope

The EU AI Act applies to providers, deployers, importers, and distributors of AI systems placed on the market or put into service within the EU. Its scope is defined by the technology (AI systems) rather than the type of data processed. The AI Act applies regardless of whether personal data is involved, covering AI systems that process only non-personal data as well.

Where They Overlap

The overlap occurs whenever an AI system processes personal data. A credit scoring algorithm that evaluates loan applications using individuals' financial histories is subject to the GDPR (because it processes personal data) and to the EU AI Act (because it is an AI system classified as high-risk under Annex III). Neither regulation alone provides complete coverage; both must be applied together.

The two regulations use different terminology to describe related roles and concepts, which can create confusion for compliance teams.

Concept	GDPR Term	EU AI Act Term
Entity responsible for the system	Data controller	Provider / Deployer
Entity acting on instructions	Data processor	(No direct equivalent; closest is downstream provider)
Affected individual	Data subject	(No specific term; references to "natural persons")
Regulatory authority	Data Protection Authority (DPA)	National competent authority / AI Office
Impact assessment	Data Protection Impact Assessment (DPIA)	Fundamental Rights Impact Assessment (FRIA) / Conformity assessment

Understanding these mapping relationships is critical for building integrated governance processes. A single organizational function, such as a compliance team or legal department, often needs to fulfil obligations under both frameworks, and aligning terminology prevents miscommunication.

Automated Decision-Making: The Core Intersection

The most significant area of overlap between the GDPR and the EU AI Act concerns automated decision-making and its impact on individuals.

GDPR Article 22 gives individuals the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning them or similarly significantly affects them. Exceptions apply where the decision is necessary for a contract, authorized by law, or based on explicit consent.

Where automated decisions are permitted, the controller must implement suitable safeguards, including the right to obtain human intervention, to express a point of view, and to contest the decision.

EU AI Act Transparency and Human Oversight

The EU AI Act's transparency obligations (Article 13) and human oversight requirements (Article 14) for high-risk AI systems complement and extend GDPR Article 22. Under the AI Act:

Deployers must inform individuals that they are subject to a decision made by or with the assistance of a high-risk AI system (Article 26(11)).
High-risk AI systems must be designed to allow effective human oversight, including the ability to override or reverse automated decisions (Article 14).
Transparency requirements mandate that the system's functioning is sufficiently understandable to deployers and, through them, to affected individuals.

The practical implication is that an organization using AI for automated decision-making must satisfy both GDPR Article 22's safeguards and the AI Act's transparency and oversight requirements. Meeting one does not automatically satisfy the other. For example, providing GDPR-compliant information about automated decision-making logic (under Articles 13 and 14 of the GDPR) does not necessarily fulfil the AI Act's more detailed transparency obligations for high-risk systems.

Data Governance: Parallel but Distinct Requirements

Both regulations impose data governance obligations, but they approach the topic from different angles.

The GDPR's data governance framework centers on the principles of lawfulness, fairness, transparency, purpose limitation, data minimization, accuracy, storage limitation, integrity and confidentiality, and accountability (Article 5). These principles govern how personal data is collected, stored, processed, and deleted.

EU AI Act Data Governance (Article 10)

The AI Act's data governance requirements under Article 10 focus specifically on the quality and representativeness of training, validation, and testing data for high-risk AI systems. Key requirements include:

Training data must be relevant, sufficiently representative, and as free of errors as possible.
Appropriate data governance measures must address data collection, data preparation, the formulation of assumptions, prior assessments of data availability, and examination of possible biases.
Where special categories of personal data (as defined in GDPR Article 9) are processed for bias detection and correction purposes, the AI Act provides a specific legal basis, subject to strict conditions and safeguards.

This last point is particularly significant. The AI Act explicitly recognizes that detecting and correcting bias in AI systems may require processing sensitive personal data, such as data revealing racial or ethnic origin, and provides a legal framework for doing so. Under the GDPR alone, processing such data is generally prohibited unless a specific exception applies. The AI Act creates an additional exception, but organizations must still comply with the GDPR's conditions for processing special category data, including implementing appropriate safeguards.

Impact Assessments: DPIAs vs FRIAs

Both regulations require impact assessments, but they serve different purposes and have different scopes.

Data Protection Impact Assessments (DPIAs)

Under GDPR Article 35, a DPIA is required when data processing is likely to result in a high risk to the rights and freedoms of individuals. DPIAs focus specifically on data protection risks and must include:

A systematic description of the processing operations and their purposes.
An assessment of the necessity and proportionality of the processing.
An assessment of risks to the rights and freedoms of data subjects.
Measures to address those risks.

Fundamental Rights Impact Assessments (FRIAs)

The EU AI Act requires deployers of high-risk AI systems (under certain conditions) to conduct a fundamental rights impact assessment before putting the system into use. FRIAs have a broader scope than DPIAs, covering not just data protection but all fundamental rights that may be affected, including:

The right to non-discrimination.
The right to an effective remedy.
Freedom of expression.
The right to human dignity.
The right to fair working conditions.
Consumer protection rights.

Organizations deploying high-risk AI systems that process personal data will likely need to conduct both a DPIA and a FRIA. While there is scope to integrate these assessments into a single process, the distinct requirements of each must be addressed. A DPIA that does not consider non-discrimination risks beyond data protection would not satisfy FRIA requirements, and a FRIA that does not address GDPR-specific data protection principles would not satisfy DPIA requirements.

For a structured approach to conducting these assessments, our EU AI Act risk assessment guide provides detailed methodology.

Enforcement: Different Bodies, Coordinated Action

The GDPR and the EU AI Act assign enforcement responsibilities to different bodies, but the regulations anticipate the need for coordination.

GDPR enforcement is handled by national Data Protection Authorities (DPAs), coordinated through the European Data Protection Board (EDPB). DPAs have extensive powers, including the ability to impose administrative fines of up to 20 million EUR or 4% of worldwide annual turnover.

EU AI Act Enforcement

The EU AI Act establishes a multi-layered enforcement structure:

The AI Office (within the European Commission) is responsible for overseeing GPAI model obligations and coordinating enforcement at the EU level.
National competent authorities (designated by each Member State) are responsible for supervising the application of the Act at the national level.
Market surveillance authorities oversee compliance for AI systems placed on the market.

In many Member States, the DPA may also be designated as the national competent authority for the AI Act, or the two bodies may operate independently but coordinate on cases involving both data protection and AI regulation.

Coordination Challenges

When an AI system violates both the GDPR and the AI Act, multiple enforcement bodies may have jurisdiction. For example, a biased credit scoring AI system could trigger:

A GDPR investigation by the DPA (for unfair processing of personal data).
An AI Act investigation by the national competent authority (for non-compliance with high-risk system requirements).
Sector-specific enforcement by a financial regulator.

Organizations should be prepared for multi-agency investigations and ensure that their compliance documentation is consistent across all frameworks.

Penalties: A Side-by-Side Comparison

Both regulations impose significant financial penalties, though the scales differ.

Violation Type	GDPR Maximum Fine	EU AI Act Maximum Fine
Most serious violations	20 million EUR or 4% of turnover	35 million EUR or 7% of turnover
Significant violations	10 million EUR or 2% of turnover	15 million EUR or 3% of turnover
Administrative/procedural	10 million EUR or 2% of turnover	7.5 million EUR or 1.5% of turnover

Importantly, these penalties can be imposed concurrently. A single AI system failure could result in fines under both the GDPR and the AI Act, along with any applicable sector-specific penalties. However, the AI Act includes a provision (Article 99(4)) stating that, where administrative fines are imposed for the same conduct under both the AI Act and the GDPR, the total amount shall not exceed the higher of the two applicable penalties. This prevents pure double jeopardy but does not eliminate the risk of significant cumulative penalties for different aspects of non-compliance.

For a detailed analysis of the AI Act's penalty framework, see our guide on EU AI Act fines and enforcement.

Building a Unified Compliance Framework

Rather than maintaining separate compliance programs for the GDPR and the EU AI Act, organizations should build an integrated framework that addresses both regulations efficiently.

Step 1: Unified AI and Data Inventory

Maintain a single register that maps all AI systems in use, the personal data they process, and the regulatory obligations that apply under both the GDPR and the AI Act. This register should identify:

The legal basis for processing personal data (GDPR).
The risk classification of each AI system (AI Act).
The roles and responsibilities under both frameworks (controller/processor under GDPR; provider/deployer under AI Act).

Step 2: Integrated Impact Assessments

Develop a combined impact assessment template that addresses both DPIA and FRIA requirements. This avoids duplicated effort while ensuring that all relevant risks are covered. The assessment should evaluate:

Data protection risks (GDPR).
Fundamental rights risks beyond data protection (AI Act).
Technical risks related to accuracy, robustness, and cybersecurity (AI Act).

Step 3: Harmonized Documentation

Align technical documentation requirements under the AI Act with records of processing activities (ROPA) under the GDPR. While the formats differ, much of the underlying information overlaps, including descriptions of data sources, processing purposes, security measures, and risk mitigation strategies.

Step 4: Coordinated Governance

Assign clear ownership for AI compliance within the existing data protection governance structure. The Data Protection Officer (DPO) and the AI compliance function should work closely together, sharing information and coordinating on issues that span both regulations. In smaller organizations, the same individual or team may fulfil both roles.

Step 5: Joint Training Programs

AI literacy training (required under AI Act Article 4) and data protection awareness training (required under GDPR accountability obligations) should be delivered as a coordinated program. Staff who interact with AI systems that process personal data need to understand their obligations under both frameworks.

The Role of Data Protection in AI Training Data

A frequently asked question concerns the legal basis for using personal data to train AI models. Under the GDPR, any processing of personal data requires a lawful basis (Article 6). For AI training data, the most commonly relied-upon bases are:

Legitimate interest (Article 6(1)(f)): Often used for training models on datasets that include personal data, subject to a balancing test against the rights of data subjects.
Consent (Article 6(1)(a)): Sometimes used but presents practical challenges for large-scale AI training due to the difficulty of obtaining informed, specific consent for broad model training purposes.
Contract performance (Article 6(1)(b)): May apply where the AI training is necessary to provide a service contracted by the data subject.

The AI Act does not override or replace GDPR requirements for training data. Organizations must have a valid GDPR legal basis for any personal data used in AI training, in addition to meeting the AI Act's data governance requirements under Article 10.

Joint Controller Scenarios in AI Value Chains

AI systems often involve complex value chains with multiple parties: model providers, platform operators, application developers, and end-user organizations. Under the GDPR, when two or more controllers jointly determine the purposes and means of processing, they are joint controllers and must enter into an arrangement under Article 26 of the GDPR.

In the AI Act context, the relationship between providers and deployers does not map neatly onto the GDPR's controller/processor framework. A GPAI model provider and a downstream deployer may both exercise meaningful control over how personal data is processed, potentially creating a joint controllership scenario under the GDPR even if they have distinct roles under the AI Act.

Organizations should carefully analyze their AI value chain relationships to determine whether joint controllership applies and, if so, ensure that the necessary GDPR arrangements are in place alongside AI Act compliance measures.

Conclusion

The GDPR and the EU AI Act are complementary regulations that together form a comprehensive framework for governing AI systems that process personal data. Neither regulation alone is sufficient. Organizations that treat them as separate compliance silos risk both regulatory gaps and wasted resources.

The most effective approach is a unified compliance framework that leverages existing GDPR processes, such as DPIAs, data governance measures, and accountability documentation, while extending them to cover the AI Act's additional requirements for risk management, transparency, human oversight, and fundamental rights protection.

For a complete checklist of AI Act compliance requirements, visit our EU AI Act Compliance Checklist. To explore how available compliance tools can support your integrated GDPR and AI Act program, see our comparison of the best EU AI Act compliance tools.

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