How do the GDPR and the AI Act relate to each other? (We assess credit or insurance risk)

government and public services

Awarding social assistance in a municipality: the FRIA and the notification

A municipality wants to deploy an AI system that sorts applications for social assistance benefits and indicates which files merit extra scrutiny before a case worker decides. The application is already listed in the public algorithm register. The question is what has to be in place before the first citizen passes through this system.

Provenance: Article 27(1) requires deployers which are bodies governed by public law, or private entities providing public services, to perform an assessment of the impact on fundamental rights that the use of a high-risk AI system referred to in Article 6(2) may produce, prior to deploying it, with the exception of systems intended to be used in the area listed in point 2 of Annex III. That assessment covers, among other elements, the categories of natural persons and groups likely to be affected, the specific risks of harm to those categories, the implementation of human oversight measures, and the measures to be taken if those risks materialise, including the arrangements for internal governance and complaint mechanisms. Article 27(3) provides that once the assessment has been performed, the deployer shall notify the market surveillance authority of its results and submit the filled-out template referred to in paragraph 5 as part of that notification, and that in the case referred to in Article 46(1) deployers may be exempt from that obligation to notify. Article 27(5) provides that the AI Office shall develop a template for a questionnaire, including through an automated tool, to facilitate deployers in complying with their obligations under this Article in a simplified manner.

We read Article 27 as making the municipality the most obvious deployer here, and as requiring the assessment to be complete before the first application runs through the system, not as an account rendered afterwards. That duty does depend first on whether this system is high-risk at all: does it help decide entitlement to social assistance, or does it stay within a preparatory or narrowly procedural task under Article 6(3), which closes its own exception again once the system profiles citizens? Answer that question before you start on paragraph 1. An entry in the public algorithm register is on our reading something different from the assessment under paragraph 1, and it does not replace notifying the market surveillance authority of the results. In practice it pays to record the citizen's complaint route and the case worker's room to depart from the signal in the same file, because paragraph 1 asks for precisely those two elements.

Editorial example. The rule above is in the Regulation. The situation was written by us to show how that rule plays out in this sector, and is not taken from a worked case in official guidance.

Article 27(1), (3) and (5)

law enforcement

Recidivism scoring in police work: when the assessment must be redone

A police service deploys an AI system that estimates the recidivism risk of a suspect, as an aid to the judgements later made by the prosecution service and the court. The model is subsequently retrained on newer investigative data and use is extended to a second region. The question is whether the assessment made for first use remains adequate.

Provenance: Article 27(1) requires deployers which are bodies governed by public law to perform, prior to deploying a high-risk AI system referred to in Article 6(2), an assessment of the impact on fundamental rights that its use may produce, with the exception of systems intended to be used in the area listed in point 2 of Annex III. That assessment consists of a description of the processes in which the system will be used, of the period and frequency of use, of the categories of natural persons and groups likely to be affected, of the specific risks of harm taking into account the information given by the provider pursuant to Article 13, of the implementation of human oversight measures according to the instructions for use, and of the measures to be taken if those risks materialise. Article 27(2) provides that the obligation applies to the first use, that previously conducted impact assessments or existing assessments carried out by the provider may be relied on in similar cases, and that a deployer who considers during use that any element listed in paragraph 1 has changed or is no longer up to date shall take the necessary steps to update the information. Article 27(3) provides that the results are notified to the market surveillance authority together with the filled-out template, and that in the case referred to in Article 46(1) an exemption from that notification duty may apply.

We read Article 27 as covering a police service as a body governed by public law under paragraph 1, and as an assessment that does not stop at first use: retraining on newer investigative data or extending use to a second region touches the elements of paragraph 1 and, on our reading, calls for updating the record. That retraining also raises a question Article 27 itself does not answer, namely whether the change goes far enough to count as a substantial modification, which would make you a provider in your own right under Article 25. Bear in mind as well that the exception in paragraph 3 concerns, on our reading, the notification and not the assessment itself. Finally, start that assessment only after establishing that the deployment as such is permitted, because Article 5 rules out certain predictive applications in criminal investigation.

Editorial example. The rule above is in the Regulation. The situation was written by us to show how that rule plays out in this sector, and is not taken from a worked case in official guidance.

Article 27(1)-(3)

education

Selection at student admission: a DPIA is not yet a FRIA

A university of applied sciences has an AI system rank applications for a vocational programme, using the exam results of earlier students to calibrate that ranking. A data protection impact assessment already exists for this processing. The question the school asks is whether that also covers the fundamental rights side of admission.

Provenance: Article 27(1) provides that deployers which are bodies governed by public law, or private entities providing public services, and deployers of high-risk AI systems referred to in points 5(b) and (c) of Annex III, shall perform an assessment of the impact on fundamental rights prior to deploying a high-risk AI system referred to in Article 6(2), with the exception of systems intended to be used in the area listed in point 2 of Annex III. That assessment consists of a description of the deployer's processes in which the system will be used in line with its intended purpose, of the period and frequency of use, of the categories of natural persons and groups likely to be affected, of the specific risks of harm to those categories, of the implementation of human oversight measures, and of the measures to be taken if those risks materialise, including the arrangements for internal governance and complaint mechanisms. Article 27(2) provides that the obligation applies to the first use, that the deployer may in similar cases rely on previously conducted impact assessments or existing assessments carried out by the provider, and that a deployer who considers during use that any element listed in paragraph 1 has changed or is no longer up to date shall take the necessary steps to update the information. Article 27(4) provides that where an obligation under this Article is already met through the data protection impact assessment conducted pursuant to Article 35 of Regulation (EU) 2016/679 or Article 27 of Directive (EU) 2016/680, the assessment under paragraph 1 complements that data protection impact assessment.

We read Article 27 as placing the education institution that runs this selection itself in the deployer role, but that alone does not settle the duty. Paragraph 1 names bodies governed by public law and private entities providing public services, and whether a state-funded or a private university of applied sciences answers to either description is the question you have to settle first. If it does, an existing data protection impact assessment is on our reading the starting point rather than the last word: paragraph 4 has the fundamental rights assessment sit alongside it, and since Regulation (EU) 2026/1744 that assessment may incorporate or cross-refer to relevant parts of it, so the real question is which elements of paragraph 1 are still missing. For you that means recording which groups of students may be affected, how the admissions committee or the teacher can correct an outcome, and where a rejected applicant can lodge a complaint. If the selection rule or the assessment component underpinning the ranking changes, that is on our reading the moment to update the record, rather than the start of the next academic year.

Editorial example. The rule above is in the Regulation. The situation was written by us to show how that rule plays out in this sector, and is not taken from a worked case in official guidance.

Article 27(1), (2) and (4)

financial services

Health insurer using AI for risk assessment: public or private makes no difference

A health insurer uses AI for risk assessment and pricing of health and life insurance. The question is whether this falls under point 5(c) of Annex III, and with that whether the Article 27 FRIA duty comes into play.

Provenance: The Commission draft guidelines of 19 May 2026 state that the health and life insurance in point 5(c) may be offered on a private or a public basis: a health insurer governed by public law also falls within it, so long as the system is intended for risk assessment or pricing with regard to natural persons. Privately serviced health insurance counts as an essential private service, even in a Member State with a public healthcare system. Unlike point 5(b), point 5(c) provides no exception for fraud detection. The document is a consultation version: non-binding and not yet final.

For the FRIA question point 5(c) counts twice: it makes the system high-risk and it makes you, as deployer, one of the parties Article 27 names. A public-law form or a public healthcare system in your Member State changes nothing there. Do not count on the fraud-detection exception from point 5(b) either: it does not apply here, and a fraud feature alongside risk assessment does not take the system out.

Draft guidelines on high-risk AI classification, 19 May 2026, annex on Annex III, paragraphs (319) to (321)

No mandatory course format, no certificate, no exam and no AI officer

The Commission Q&A on AI literacy states that there is no one size fits all when it comes to AI literacy and that no strict requirements or mandatory trainings are imposed. On certification, the Q&A states literally that there is no need for a certificate and that organisations can keep an internal record of trainings or other guiding initiatives. On assessment, it states that Article 4 of the AI Act does not entail an obligation to measure the AI knowledge of employees. On governance, it states that no specific governance structure is mandated to comply with Article 4, so that unlike the data protection officer under the GDPR, no AI officer needs to be appointed. On the level, the Q&A states that following the Digital Omnibus amendment AI literacy remains an obligation for providers and deployers of AI systems, but that no specific or sufficient level is mandated and that the Regulation does not require guaranteeing any specific level of AI literacy of any individual. Against that, the Q&A states that simply relying on the AI systems' instructions for use or asking staff to read them might be ineffective, and that organisations should take into account general AI understanding within the organisation, whether they are a provider or a deployer, the risks associated with the systems deployed, staff knowledge gaps considering technical knowledge, experience, education and training, and contextual factors such as sector, purpose and affected populations. The Q&A further states that organisations may implement different levels of training or learning approaches depending on knowledge, experience, education and role, and that staff with a degree or experience in AI development are normally considered AI literate, while the organisation must still verify that those persons understand the specific AI systems of the organisation, know how to deal with them and are aware of all risks.

Commission Q&A on AI literacy, sections on required level, training formats, certificates, assessment of knowledge and governance structures (consulted 9 August 2026)

Article 4 reaches beyond your own staff, and the national supervisor enforces it

The Commission Q&A on AI literacy states that Article 4 applies to providers and deployers of AI systems and in addition to other persons dealing with the operation and use of AI systems on their behalf, covering persons broadly within the organisational remit, with a contractor, a service provider and a client given as examples. On clients, the Q&A states that they may need AI literacy depending on the specific risk, reasoning that affected persons should understand how decisions taken with the assistance of AI will have an impact on them. On geographic scope, the Q&A states that the AI Act's legal framework applies to both public and private actors inside and outside the EU as long as the AI system is placed on the Union market, used in the Union, or its use has an impact on people located in the EU. On supervision, the Q&A states that the supervision and enforcement of Article 4 is not with the AI Office but under the remit of national market surveillance authorities, and that supervision and enforcement began on 2 August 2026, while Article 4 itself entered into application on 2 February 2025. On sanctions, the Q&A states that national market surveillance authorities could impose penalties and other enforcement measures for infringements of Article 4, that this will be based on national laws that Member States were due to adopt by 2 August 2025, that any sanction must be proportionate and based on the individual case taking into account factors such as the nature and gravity of the infringement and its intentional or negligent character, and that sanctions are more likely if there is proof of an incident due to a lack of appropriate training and guidance. Article 4 is not listed in the enumeration in Article 99(4) of the AI Act, which covers only Articles 16, 22, 23, 24, 26, 31, 33(1), (3) and (4), 34 and 50, so the level of any penalty for Article 4 follows from national law rather than from the Regulation's own ceilings. The Q&A further states that Article 4 reinforces the transparency provisions of Article 13 and the human oversight provisions of Article 14 and indirectly contributes to the protection of affected persons, and that for deployers of high-risk systems the Article 26 obligation to ensure staff are trained to ensure human oversight is a distinct requirement; that requirement becomes applicable on 2 December 2027 for standalone Annex III systems and on 2 August 2028 for Annex I systems.

Commission Q&A on AI literacy, sections on target groups, geographic scope, supervision and enforcement, and sanctions (consulted 9 August 2026)

Article 6 has two separate routes to high-risk

The European Commission's draft guidelines on the classification of high-risk AI of 19 May 2026, which are expressly non-binding, state in paragraph (7) that an AI system is high-risk in two scenarios: first, where it is intended to be used as a safety component of a product, or is itself a product, covered by the Union harmonisation legislation listed in Annex I and required to undergo third-party conformity assessment; and second, where it falls within one of the use cases in the areas listed in Annex III. Paragraph (448) of those same draft guidelines notes that the Article 113 application dates have been postponed by the AI Omnibus to 2 December 2027 for the Article 6(2) route and 2 August 2028 for the Article 6(1) route.

Draft guidelines on high-risk AI classification (19 May 2026), General principles chapter, section II, paragraph (7); section V, paragraph (448)

Broadly positioned and general purpose AI systems: a disclaimer is not enough

According to the non-binding draft guidelines of 19 May 2026 on the classification of high-risk AI, paragraph (12) provides that where the instructions for use, contractual arrangements, terms of service, usage policy, promotional and sales materials or technical documentation present the AI system as broadly applicable across a generality of contexts and functions, and do not consistently limit its application or exclude high-risk uses, the system's intended purpose will be deemed to also encompass high-risk use cases and therefore qualify as high-risk. Under these draft guidelines this applies in particular where such uses are feasible and reasonably foreseeable given the system's functionalities and capabilities. The same paragraph states that merely asserting, for example in the terms of service, that high-risk uses are excluded is insufficient where the provider's overall presentation, examples or product positioning effectively provides for or promotes such uses, and that any limitations of use must be described clearly, concretely and coherently across all materials.

Draft guidelines on high-risk AI classification (19 May 2026), General principles chapter, section II.2, paragraph (12)

ISO/IEC 5259 series: data quality for analytics and machine learning

The ISO/IEC 5259 series (Artificial intelligence: Data quality for analytics and machine learning) comprises five parts: part 1 (overview, terminology and examples), part 2 (data quality measures), part 3 (data quality management requirements and guidelines) and part 4 (data quality process framework), all published in 2024, plus part 5 (data quality governance framework), published in February 2025. CEN-CENELEC has adopted parts as European standards, including EN ISO/IEC 5259-4:2025 and EN ISO/IEC 5259-3:2025. No part is cited in the Official Journal, so no presumption of conformity under Article 40 arises. The deliverable intended to do so for Article 10 is prEN 18284.

prEN 18284: quality and governance of datasets in AI

prEN 18284 (Artificial intelligence: Quality and governance of datasets in AI) is the JTC 21 deliverable under M/613 for Article 10 of the AI Act, which sets requirements for the training, validation and testing datasets of high-risk AI systems. As at June 2026 the deliverable was at the drafting stage. It has not yet been published as an EN and is not cited in the Official Journal.