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Sprint projectFeb 2, 2026Brooklyn

Systematic Cross-Regulation Threat Topology for EU AI Governance

Rian Czerwiński, Wiktoria Leks · Team Convent

Submitted to The Technical AI Governance Challenge. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Systematic Cross-Regulation Threat Topology for EU AI Governance

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A single frontier AI training run can simultaneously trigger obligations under the EU AI Act, GDPR, Copyright Directive, and NIS, yet no systematic framework maps these compounding regulatory threats across stakeholder types and jurisdictions. We present a systematic threat topology covering 19 EU-level regulations across five stakeholder categories (frontier model developers, deployment platforms, hardware providers, open-source developers, and research organizations), with geographic enforcement modifiers for all 27 Member States. Our methodology employs an activity-based stakeholder taxonomy, temporal activation mapping, and a KNOW/GUESS/UNKNOWN epistemic framework that quantifies regulatory uncertainty rather than obscuring it. Key findings include: (1) cross-regulation compounding creates multiplicative compliance surfaces where identical development activities trigger 3–5 regulatory regimes simultaneously; (2) enforcement concentration: five DPAs account for over 85% of €5.88B in cumulative GDPR fines, creates significant compliance cost differentials depending on establishment jurisdiction; (3) temporal cascading between February 2025 and August 2027 activates obligations under four major regulatory categories in overlapping waves, with August 2025 marking a critical inflection point for frontier AI providers. We release the full 41-page threat matrix as open infrastructure for practitioners navigating EU AI compliance during this implementation period.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. This seems like ambitious work that could aid a variety of stakeholders in complying with a variety of EU AI regulations. I encourage you to seek feedback from any applicable stakeholders to understand better if this would be useful in practice. I expect that existing stakeholders have a complex process for staying on top of regulations and it is unclear how this work fits into that… or is this intended to be more helpful to a new stakeholder who has only just begun to develop their own processes?

    One issue to consider is how to keep this document up-to-date, as the value to the reader depends on it being current. Similarly, I wonder how existing stakeholders manage this problem.

  2. The paper’s research topic is important and interesting - practitioners really do seem to be exposed to multiple overlapping regulations. However, the analysis of interactions itself is undersupplied in the paper: there are multiple claims that cross-regulation compounding has multiplicative effects, but it doesn’t actually show what those effects are and why they happen. E.g. the tension between GDPR disclosure and AI Act transparency is mentioned but never developed. Also, we're told about the KNOW/GUESS/UNKNOWN framework but we aren’t shown a breakdown of what proportion of the matrix falls into each category, or how this varies by stakeholder type.

Cite this project

@misc{czerwinski2026systematic,
  title = {{Systematic Cross-Regulation Threat Topology for EU AI Governance}},
  author = {Rian Czerwiński and Wiktoria Leks},
  year = {2026},
  month = feb,
  note = {Submitted to The Technical AI Governance Challenge, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/systematic-crossregulation-threat-topology-for-eu-ai-governance-otso}},
  url = {https://apartresearch.com/sprints/projects/systematic-crossregulation-threat-topology-for-eu-ai-governance-otso}
}

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