EU AI Act vs GDPR: Comprehensive Comparison and Compliance Guide

Understanding two key EU regulations

The EU AI Act (Regulation 2024/1689) and the General Data Protection Regulation (GDPR) are two of Europe's most important regulatory frameworks. Both significantly impact how organizations develop, deploy, and use technology — but they govern different aspects and impose different obligations.

The GDPR, which entered into force in 2018, focuses on protecting personal data and the rights of individuals whose data is processed. The EU AI Act, which entered into force in August 2024, focuses on managing risks posed by artificial intelligence systems — particularly high-risk uses that could harm fundamental rights.

Many organizations must comply with both regulations simultaneously, especially those that develop or deploy AI systems that process personal data. Understanding how they differ, where they overlap, and how to manage both is essential for effective compliance.

Scope and applicability: What does each regulate?

The two regulations apply to different phenomena and therefore have different scopes.

EU AI Act — What it covers

The EU AI Act applies to AI systems — technical tools that use machine learning, logic-based systems, or other AI techniques to make decisions, predictions, or recommendations that affect people. It applies to:

• Prohibited AI systems — AI that poses unacceptable risk to fundamental rights (social scoring, emotion recognition in workplaces, real-time biometric identification in public spaces).
• High-risk AI systems — AI in sensitive domains like employment, education, law enforcement, and critical infrastructure (Annex III categories).
• Limited-risk AI systems — AI that must provide transparency notices (like chatbots). Providers must disclose that the system is AI-generated.
• General-purpose AI models (GPAI) — Large AI models (like GPT or Llama) that are general-purpose and carry significant capabilities and unpredictable uses.

GDPR — What it covers

The GDPR applies to personal data — any information relating to an identified or identifiable natural person. It applies to any organization that:

• Processes personal data of residents in the EU, regardless of where the organization is located.
• Offers goods or services to EU residents — even if the organization is outside the EU.
• Monitors the behavior of EU residents — such as through tracking, profiling, or surveillance.
• Collects, stores, uses, or shares personal data — whether in a database, CRM system, email list, or analytics platform.

Key differences: A side-by-side comparison

While both are EU regulations designed to protect people, the EU AI Act and GDPR differ fundamentally in what they regulate and how they approach enforcement.

Where they overlap: AI systems processing personal data

The two regulations overlap when an AI system processes personal data. In such cases, organizations must comply with requirements from both regulations simultaneously.

Examples of overlap include AI systems that analyze personal data to make automated decisions (hiring, credit scoring, medical diagnosis), chatbots that interact with users' data, biometric systems that identify individuals, and recommender systems that profile users.

Example 1: Hiring AI that uses personal data

A company builds an AI system that analyzes job applications to rank candidates. The system processes personal data (names, work history, education). This scenario triggers both the EU AI Act and GDPR: Under the EU AI Act, recruiting AI is high-risk (Annex III, category 2), so the company must conduct risk assessments, maintain technical documentation, and implement human oversight. Under GDPR, the company must obtain legal basis for processing (consent or legitimate interest), provide candidates with information about automated processing, and allow candidates to request human review of decisions.

Example 2: Chatbot customer support

A company deploys a chatbot that converses with customers and learns from conversations to improve responses. The system processes customer personal data (names, account information, interaction history). Under the EU AI Act, if the chatbot's recommendations could significantly impact users (e.g., financial advice), it may be high-risk, requiring risk management and transparency. Under GDPR, the company must obtain legal basis for processing chat logs, provide privacy notices to users, implement data subject rights (access, deletion), and implement data protection by design.

Example 3: Biometric identification system

An organization uses facial recognition AI to authenticate users accessing secure facilities. The system processes biometric personal data. Under the EU AI Act, real-time biometric identification in public spaces is prohibited (Article 5); even in private settings, it is high-risk and requires strict safeguards. Under GDPR, biometric data is a special category (sensitive data) requiring explicit consent, and the organization must conduct a Data Protection Impact Assessment (DPIA) before deployment.

DPIA vs FRIA: Two assessment frameworks

Both regulations require organizations to assess risks before deploying systems that could impact individuals. However, the assessments serve different purposes and follow different methodologies.

Data Protection Impact Assessment (DPIA) — GDPR Article 35

A DPIA is required before processing personal data in ways that pose high risk to data subject rights. The DPIA evaluates:

• The necessity and proportionality of the data processing
• Risks to individuals (privacy breaches, unauthorized access, loss of control)
• Mitigation measures (encryption, access controls, consent mechanisms)
• Consultation with data protection authorities if risks cannot be adequately mitigated

Fundamental Rights Impact Assessment (FRIA) — EU AI Act Article 27

A FRIA is required for high-risk AI systems that process personal data or interact with individuals in ways that could affect fundamental rights. The FRIA evaluates:

• Impacts on fundamental rights (privacy, non-discrimination, due process, freedom of expression)
• Potential for bias, errors, or discrimination in AI decisions
• Risk of circumventing user consent or rights
• Mitigation measures and ongoing monitoring

How DPIA and FRIA work together

In practice, organizations deploying high-risk AI that processes personal data should conduct both assessments — they serve complementary purposes and reinforce each other:

• Different starting points: DPIA focuses on protecting data and personal autonomy; FRIA focuses on protecting fundamental rights and human dignity in the context of AI.
• Different scope: DPIA is required for high-risk data processing; FRIA is required for all high-risk AI, whether or not personal data is involved.
• Complementary content: A comprehensive assessment includes elements of both — data protection safeguards (DPIA) and AI-specific safeguards like bias testing, human review mechanisms, and model explainability (FRIA).
• Best practice: Many organizations now conduct a single, integrated assessment that addresses both GDPR Article 35 and EU AI Act Article 27 requirements, saving time and effort while ensuring comprehensive risk management.

Compliance strategy for organizations subject to both regulations

If your organization develops or uses AI systems that process personal data, here's a practical roadmap to achieve compliance with both the EU AI Act and GDPR.

Step 1: Classify your AI systems

Use tools like Conformy's classifier to determine whether your AI systems fall under the EU AI Act. Identify which systems are prohibited, high-risk, limited-risk, or minimal-risk. For high-risk systems, start technical documentation and risk assessments immediately — the high-risk deadline is August 2, 2026.

Step 2: Identify personal data processing

For each AI system, determine whether it processes personal data. If yes, audit the legal basis for processing (consent, contract, legitimate interest, legal obligation, vital interests). Document the categories of personal data, retention periods, and recipients. Identify special categories (biometric, health, genetic data) that require explicit consent.

Step 3: Conduct integrated assessments

For high-risk AI processing personal data, conduct a single, integrated risk assessment covering both EU AI Act Article 27 requirements (FRIA) and GDPR Article 35 requirements (DPIA). Assess impacts on fundamental rights, data protection risks, likelihood of errors or bias, and mitigation measures.

Step 4: Implement governance and technical safeguards

Establish governance: assign responsibility for AI compliance, document processes, and implement audit trails. Implement technical safeguards: encryption, access controls, anonymization (where appropriate), bias testing, human oversight mechanisms, and automated decision-making safeguards. For sensitive decisions (employment, credit, justice), ensure human review is feasible.

Step 5: Secure appropriate legal basis and transparency

Ensure you have a valid legal basis under GDPR for the data processing (usually contract or legitimate interest). Provide privacy notices informing individuals that their data is processed by AI, what decisions the AI makes, and what rights they have. Under GDPR Article 22, provide meaningful information about logic, significance, and consequences of automated processing and allow individuals to request human review.

Step 6: Prepare for ongoing compliance and incident response

Establish data subject rights processes (access, correction, deletion). Implement breach notification procedures (notification to authorities within 72 hours and affected individuals without undue delay). Document post-market monitoring and re-assess systems when significant changes occur. Train staff on both AI and data protection obligations.

Frequently asked questions