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Short answer: The EU AI Act regulates artificial-intelligence systems, general-purpose AI models, their uses and the organizations that provide or deploy them. The GDPR regulates processing of personal data. A business can therefore need to comply with both at the same time: an AI Act assessment does not prove GDPR compliance, and GDPR compliance does not prove AI Act compliance.
The practical test is to examine both the AI system and the data flows around it. Classify the use under the AI Act, identify your role, then determine whether personal data enters prompts, training sets, outputs, logs, monitoring or support processes. Apply the controls required by each regime and keep the evidence together.
What each law regulates
The EU AI Act regulates AI systems, models and conduct
Regulation (EU) 2024/1689, the EU AI Act, attaches duties to covered AI systems and general-purpose AI models, the way they are used, and the role an organization plays. Roles can include provider, deployer, importer, distributor and, in some cases, a product manufacturer that integrates an AI system.
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The Act is risk-based. It distinguishes prohibited AI practices, high-risk systems, systems subject to specific transparency duties and lower-risk applications. A system is not “high risk” merely because it uses machine learning or a large model; classification depends on the use, context and the provisions that apply to that system.
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The GDPR regulates personal-data processing
Regulation (EU) 2016/679, the General Data Protection Regulation, applies when an organization processes personal data within its territorial and material scope. It governs the purposes and means of processing, the rights of individuals and the safeguards around their data.
Its core principles include lawfulness, fairness and transparency, purpose limitation, data minimisation, accuracy, storage limitation, security and accountability. Depending on the operation, businesses may also need a lawful basis, privacy information, records of processing, processor contracts, procedures for data-subject rights and a data-protection impact assessment.
EU AI Act vs. GDPR: side-by-side comparison
| Comparison point | EU AI Act | GDPR |
|---|---|---|
| Regulatory object | AI systems, general-purpose AI models, specified practices and the organizations placing, putting into service, distributing or using covered systems. | Processing of personal data and the rights and duties connected with that processing. |
| Main trigger | A covered AI system or model, its use, the organization’s role and the applicable risk category. | Personal data being processed in an activity within GDPR scope. |
| Business roles | Provider, deployer, importer, distributor and certain product manufacturers. | Controller, joint controller or processor, determined for each processing operation. |
| Compliance structure | Prohibitions, risk classification, high-risk requirements, transparency duties, documentation, human oversight and market surveillance. | Lawful and fair processing, transparency, minimisation, accuracy, retention limits, security, accountability and data-subject rights. |
| Personal-data relationship | Does not displace EU data-protection law. AI-related personal-data processing remains subject to the GDPR. | Continues to apply when an AI activity processes personal data. |
| General application timing | Phased application, with different dates for definitions and AI literacy, prohibitions, governance and general-purpose models, general obligations, and certain high-risk systems. | Applied since 25 May 2018; regulator guidance and national enforcement practice continue to develop. |
Can a business be subject to both laws?
Yes. The same deployment can create obligations under both regimes, but each law asks a different question. The AI Act asks what AI system is being used, for what purpose, by whom and at what risk level. The GDPR asks what personal data is processed, why, on whose instructions, for how long and with what safeguards.
Article 2(7) of the AI Act makes the relationship explicit: “Union law on the protection of personal data, privacy and the confidentiality of communications applies to personal data processed in connection with the rights and obligations laid down in this Regulation.” This means an AI Act conformity assessment, registration or risk control cannot be treated as a substitute for a GDPR analysis.
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Conversely, a company can have a lawful basis and appropriate GDPR controls while still breaching an AI Act prohibition, missing a required transparency notice or misclassifying a high-risk system. Run the assessments together, but do not merge their legal tests.
How the AI Act’s risk model changes your obligations
Prohibited practices
Some AI practices are prohibited outright. The relevant question is whether the specific use falls within a prohibited category, not whether the underlying model is marketed as safe or general purpose. Stop the use and obtain specialist legal advice if a proposed feature appears to manipulate, exploit or otherwise fall within a prohibited practice before putting it into operation.
High-risk AI systems
High-risk status is tied to the Act’s criteria, including Article 6 and the relevant annexes. It can arise because an AI system is a safety component of a regulated product or because it is used in a listed area such as employment, education, access to essential services, law enforcement, migration or the administration of justice.
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Where the high-risk rules apply, expect a more formal control framework: risk management, data and data-governance controls, technical documentation, record-keeping, accuracy and cybersecurity measures, human oversight, quality-management responsibilities and post-market monitoring. The exact duties depend on the system and the organization’s role.
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Transparency obligations
Some systems are not prohibited or high risk but still attract transparency duties, including certain interactions with people, generated or manipulated content and emotion-recognition or biometric-categorisation contexts covered by the Act. Check the applicable Article 50 requirements rather than assuming that a general “AI-generated” label satisfies every use case.
Lower-risk systems and general-purpose models
Lower-risk applications may not carry the full high-risk framework, but the Act still includes obligations in specified circumstances, including AI literacy and rules for general-purpose AI models. A general-purpose model supplied to another organization can create provider obligations, while a business using it in a particular workflow may have separate deployer duties.
When GDPR applies to an AI deployment
GDPR is engaged when personal data is processed. That can be obvious, such as a customer’s name in a support prompt, or easy to miss, such as an identifiable voice recording, an employee identifier in a fine-tuning file, a face in an image, a pseudonymous account ID or a prompt retained in a vendor’s log.
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- Inputs and prompts: customer messages, employee records, CVs, medical details, identifiers, images, audio or inferred attributes.
- Training and fine-tuning: source datasets, human feedback, labels, embeddings and test records.
- Evaluation: benchmark samples, red-team cases and quality-review transcripts.
- Outputs: generated text, rankings, recommendations, classifications, profiles or decisions about identifiable people.
- Logs and telemetry: prompts, outputs, account details, IP addresses, device data, timestamps and diagnostic traces.
- Support and monitoring: tickets, screenshots, incident records and staff review notes.
If an activity genuinely processes no personal data, GDPR may not be engaged by that activity. That conclusion must follow from the actual data flows, not from a vendor’s statement that a model is “anonymous” or “privacy friendly.” Other laws and the AI Act can still apply.
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Business roles are different under each regime
AI Act roles
Determine whether you are a provider, deployer, importer, distributor or another covered operator. A company that merely buys a tool may be a deployer, but that is not automatic. Developing a system in-house, placing it on the market under your own name, making a substantial modification or integrating it into a regulated product can change the role and the resulting duties.
GDPR roles
For each processing operation, decide whether your organization determines the purposes and essential means (controller), does so jointly with another organization (joint controllers), or processes data on another party’s documented instructions (processor). The same company can be a controller for employee-use monitoring, a processor for a customer’s support workflow and a joint controller for a jointly determined service.
Do not use one role label for the entire AI program. Map roles to each system, purpose, dataset and vendor relationship.
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- Inventory the use. Record the system or model, supplier, intended purpose, users, affected people, geography, deployment date and whether the system is embedded in a regulated product.
- Map the data. Document what enters prompts, training and fine-tuning data, evaluation sets, outputs, logs, monitoring and support. Mark direct identifiers, indirect identifiers, special-category data and data about children.
- Classify the AI use. Check for a prohibited practice, high-risk status under Article 6 and the relevant annexes, Article 50 transparency duties, general-purpose-model rules and any category outside those provisions. Classification turns on use and context, not only the model’s marketing label.
- Assign roles. Establish the AI Act role for each organization and separately establish the GDPR controller, joint-controller or processor relationship for each processing operation.
- Set the GDPR foundation. Identify the lawful basis, provide privacy information, minimise the data, set retention periods, restrict access, define rights-handling procedures and assess whether a data-protection impact assessment is required.
- Set AI Act controls. Where applicable, implement risk management, technical documentation, logging, human oversight, accuracy and cybersecurity controls, transparency notices, incident escalation and post-market monitoring.
- Join the evidence. Maintain one governance record linking system documentation, vendor terms, data-protection records, access and retention controls, human-oversight procedures, incidents and review dates. This joined record is an implementation approach, not a single statutory checklist.
- Recheck before material change. Reassess after a model change, new purpose, new data source, new geography, new output type, substantial modification or change in supplier terms.
Application dates and what to verify
EUR-Lex’s application summary states that the AI Act generally applies from 2 August 2026. The staged timetable includes the following dates:
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| Date | Application point | Qualification |
|---|---|---|
| 2 February 2025 | Definitions and AI-literacy obligations, and prohibitions identified for that date. | The summary identifies a stated exception applying from 2 December 2026. |
| 2 August 2025 | Selected governance, penalty and general-purpose AI model rules. | Only the provisions covered by the relevant transitional rules apply on this date. |
| 2 August 2026 | General AI Act application. | This is the general date in the EUR-Lex summary, subject to the Act’s specific transitional provisions. |
| 2 December 2027 | Requirements for relevant Annex III high-risk systems. | Confirm whether a system falls within the applicable Annex III category and transitional rule. |
| 2 August 2028 | Relevant Annex I product-system requirements. | Applies to the product-system situations covered by the Act and its transition provisions. |
The European Commission’s report adopted on 20 May 2026 describes the four risk categories and notes that guidance, standards and practical classification experience were still developing. It also records a proposed “Digital Omnibus on AI” timeline adjustment that remained under negotiation and required agreement by Parliament and Council. Use the enacted text and current EUR-Lex dates for planning, and verify amendments and official guidance before setting a deadline or advising a client.
Examples of how the two regimes overlap
Internal recruitment assistant
An assistant that screens CVs can process names, employment histories, disability information or inferred characteristics. The business must establish its GDPR role, lawful basis, transparency and retention controls. It must also determine whether the employment use is high risk under the AI Act, apply the required controls if it is, and provide any applicable AI transparency information. Calling the tool “just a productivity assistant” does not resolve either analysis.
Customer-service chatbot
A chatbot may process account details and conversation histories under GDPR. Depending on its functionality, the AI Act may impose transparency duties so people know they are interacting with an AI system. Restricting prompts, deleting logs on a defined schedule and controlling vendor access address GDPR concerns but do not, by themselves, establish AI Act compliance.
Analytics on anonymised business data
If the data and outputs cannot reasonably be linked to an identified or identifiable person, GDPR may not apply to that processing. Document how that conclusion was reached and monitor re-identification risk. The AI Act can still apply based on the system’s use, role and risk category.
Common mistakes and how to avoid them
- Treating the AI Act as a replacement for GDPR: keep separate legal tests and evidence, connected through a shared governance record.
- Calling every AI system high risk: classify the concrete use under Article 6 and the relevant annexes.
- Relying on a “no training” promise: check prompts, retention, support access, telemetry, evaluation and account data in the contract and technical settings.
- Assuming pseudonymisation removes GDPR: pseudonymous data can remain personal data when a person can be singled out or re-identified with additional information.
- Using one role for every workflow: determine AI Act and GDPR roles separately for each system and purpose.
- Setting deadlines from an old checklist: the AI Act is phased, and implementation guidance and legislative proposals can change. Verify the consolidated law and current Commission material before relying on a date.
- Ignoring outputs: generated recommendations, rankings or profiles can contain personal data or affect people even when the prompt did not include an obvious identifier.
What a defensible compliance file should contain
For each AI use, retain a concise, current file containing:
- System name, version, supplier, owner and intended purpose.
- AI Act classification, role analysis and the reasons supporting it.
- Data-flow map covering inputs, training, evaluation, outputs, logs and support.
- GDPR controller or processor analysis, lawful basis, privacy notice and retention schedule.
- Security, access-control, deletion and vendor-contract settings.
- Human-oversight instructions, user training and escalation contacts.
- Testing, monitoring, incidents, complaints, rights requests and corrective actions.
- Review triggers and the next scheduled reassessment date.
This file helps demonstrate accountability, but it does not guarantee that a deployment is lawful. The classification, data flows and controls must match what the system actually does in production.
Bottom line for businesses using AI
Start with two parallel questions: “What AI system and use are we operating under the AI Act?” and “What personal data are we processing under the GDPR?” Answer them for each workflow, assign the correct roles, apply the stricter applicable controls and revisit the analysis whenever the model, purpose, data or supplier changes. The AI Act and GDPR are complementary regimes, not alternatives.
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