Opinion 17/2019 on the UK data protection supervisory authority draft accreditation requirements for a code of conduct monitoring body pursuant to article 41 GDPR

2 December 2019

Stellungnahme 17/2019 zum Entwurf der britischen Datenschutzaufsichtsbehörde über die Anforderungen an die Akkreditierung einer Stelle für die Überwachung der Einhaltung der Verhaltensregeln gemäß Artikel 41 DSGVO 205.5KB

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EDPB

Accreditation

Opinion 11/2025 on the draft decision of the Dutch Supervisory Authority regarding the Controller Binding Corporate Rules of the Signify Group

6 June 2025

Opinion of the Board (Art. 64)

Opinion 12/2025 on the draft decision of the Dutch Supervisory Authority regarding the Controller Binding Corporate Rules of the ASML Group Adopted on

6 June 2025

Opinion of the Board (Art. 64)

Guidelines 02/2024 on Article 48 GDPR

5 June 2025

Guidelines

Law & Compliance in AI Security & Data Protection

5 June 2025

Support Pool of Experts projects

Training curriculum on AI and data protection

Law & Compliance in AI Security & Data Protection is a training for data protection officers (DPOs) addressing the current AI needs and skill gaps.

The EDPB launched this project in the context of the Support Pool of Experts (SPE) programme at the request of the Hellenic Data Protection Authority (HDPA).

The project was completed by external expert Dr. Marco ALMADA in December 2024.

Objective

The training provides a comprehensive foundation on legal and compliance issues in AI security and personal data protection. It offers a deeper understanding through practical case studies primarily focused on the General Data Protection Regulation (GDPR), the Artificial Intelligence Act (AI Act), the Data Act, and other relevant European regulations.

Modules:

basic concepts of AI and the issues they raise for data protection law;
risks that take place at various stages of the life cycle of an AI-based tool, from the initial decision to make use of such a technology to the end of its operation;
in-depth treatment of selected topics that are critical for organisations intending to use AI systems in accordance with the requirements of data protection law.

Further information:

This project will be soon made available into a community version. This page will be updated as soon as this new version is made available to the public.

The Board decided to publish both documents as PDF files. Taking into account the very fast evolution of AI, the EDPB also decided to launch a new innovative initiative as a one-year pilot project consisting of a modifiable community version of the reports. The EDPB will start working with the authors of both reports to import them in its Git repository** to allow, in a near future, any external contributor, with an account on this platform and under the condition of the Creative Commons Attribution-ShareAlike license, to propose changes or add comments to the documents.

Fundamentals of Secure AI Systems with Personal Data

5 June 2025

Support Pool of Experts projects

Training curriculum on AI and data protection

Fundamentals of Secure AI Systems with Personal Data is a training for cybersecurity professionals, developers and deployers of AI systems on AI security & Personal Data Protection addressing the current AI needs and skill gaps.

The EDPB launched this project in the context of the Support Pool of Experts (SPE) programme at the request of the Hellenic Data Protection Authority (HDPA).

The project was completed by external expert Dr. Enrico GLEREAN in December 2024, updated in April 2025.

This project will be soon expended into a community version. This page will be update as soon as this new version is made available to the public.

Objective

The training aims to help cybersecurity professionals, developers, and deployers of high-risk AI systems reskill and upskill their technical capacity to design and implement advanced AI-enabled and data-driven systems. It integrates data protection and privacy principles with fundamental AI ethics principles in AI development.

Modules:

basic terminology of AI systems and their life-cycle;
the data privacy aspects of the AI systems life-cycle: data collection, privacy enhancing technologies, and in general, (personal) data flows before putting the system in production;
development of the AI system, considering good coding practices, secure sandboxes, and security testing of AI systems under development;
deployment of an AI system, monitoring, sustainability, and decommissioning;
checklists for auditing with a series of use cases and a deeper overview of the technological, legal, and ethical challenges in the field.

Further information:

This project will be soon made available into a community version. This page will be updated as soon as this new version is made available to the public.