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Praxikon

Knowledge platform for the GDPR and the EU AI Act

The GDPR and the EU AI Act, explained from the source.

Praxikon explains the GDPR and the EU AI Act from the official text: what it says, what it means and what you have to be able to show. Open access, with source and date for every statement.

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Want to know which AI Act obligations apply to your own AI system? The assessment needs no account and the dossier stays on your device. Assess your AI system

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Try:Our chatbot talks to customersWe screen CVs with AIDo I need a DPIA?Article 22 GDPRAll direct answers

Open access. No account required. Every statement shows its official source, legal status and the date of the latest review.

Two regulations, one source

Start with the law your question is about

In force now

Shifted (Digital Omnibus)

The delay is runway for register and evidence, not a pause.

Next date

2 December 2026

End of the transition for machine-readable marking (Art. 50(2)) and new prohibitions on non-consensual synthetic content.

Track changes in the Monitor

About the platform

What Praxikon is

Praxikon is a free knowledge platform on the GDPR and the EU AI Act. It turns both regulations into what you have to do and have to be able to show: the official text of each article, searchable and with the overlap between both laws, the obligations that apply to an AI system, the actions and evidence that belong with them, and what a regulatory change does to all of that. Every answer traces back to the official EU source, with version and date.

Why Praxikon exists, and how it works

AI Act Monitor

What is materially changing now

Not an unedited news stream, but verified changes that affect obligations, planning or evidence.

Last editorial review:

View all changesRead the methodology
  2. ApplicableAction required

    Enforcement of GPAI obligations has started

    What changed

    Since 2 August 2026, the Commission can enforce the obligations for providers of general-purpose AI models. The substantive obligations have applied to new models since 2 August 2025.

    First next step

    Record whether the organisation is a general-purpose AI provider, a downstream provider or only a deployer.

    Impact and evidenceOfficial source

Obligation to evidence

Five core routes to a defensible record

Do not start with an isolated document. First determine the applicable obligation, take the appropriate action and then retain the evidence.

  1. Article 4AI literacy and evidence of measuresQuestion to answerWhich role-, context- and risk-based measures are appropriate?Evidence to retainRole matrix, AI inventory, measures log and periodic review.
  2. Article 50Transparency for AI interaction and contentQuestion to answerWhich disclosure, marking or visible information is required?Evidence to retainApplicability decision, disclosure text, owner and review evidence.
  3. Annex IIIHigh-risk classificationQuestion to answerDoes the use fall within a high-risk domain and does the Article 6(3) filter apply?Evidence to retainClassification rationale, filter analysis, registration and reassessment trigger.
  4. Article 27Fundamental rights impact assessmentQuestion to answerIs a FRIA required and how should it connect to the DPIA?Evidence to retainFRIA report, affected-person impact, measures and decision record.
  5. Articles 51-55GPAI and systemic-risk modelsQuestion to answerWhat is your role around the model and which additional duties follow?Evidence to retainTechnical information, copyright policy, downstream information and risk file.
Not sure which route applies?View public templates

Explore the analysis

Open specialist source routes when the concise obligation card does not provide enough detail.

Execution

All knowledge here is free. You do not have to do the execution alone.

Embed AI

Privacy, DPIA and AI governance

For the organisation that wants to turn the GDPR and the AI Act into working practice: privacy advice, DPIAs, data processing agreements, AI governance or interim support by Zahed Ashkara.

See privacy and AI advice by Embed AI

LearnWize

AI literacy demonstrable per role

For the people behind the systems: role-based learning paths and assessment under Article 4, with an audit-ready file per employee.

See LearnWize

Editorial methodology

Trust should be verifiable

Our value is not more text, but traceable selection, visible freshness and a consistent path from source to action.

  1. Official sources first

    Every material change links to at least one canonical source, such as EUR-Lex, the European Commission or the AI Office.

  2. Facts and analysis remain separate

    We distinguish what the source establishes from our editorial analysis and possible next steps.

  3. Human review before publication

    Automated checks help identify changes. Publication follows only after source, status and materiality have been reviewed.

  4. Open and verifiable

    Reading, searching, checking sources and using core routes requires no account. Review dates and correction routes remain visible.

Read the full methodologyReport a correction or missing source

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