SPRINKLING ACT
Author profile
Founder & AI Regulation and RegTech Analyst at Sprinkling Act.
BIO

I build the pre-conformity layer for the EU AI Act: an independent, article-by-article position assessment, built to hold up under enforcement. First in the chain, before the lawyer drafts, the auditor reviews, or the regulator asks. I delimit, I don't recommend.
AI regulation and market intelligence analyst based in Brussels, focused on how regulatory frameworks reshape real-world AI deployment and market dynamics.
Author of the EU AI Act Readiness Report (2026), a structured screening of 50 European AI companies that identifies a systemic classification gap in how organizations determine and operationalize their regulatory obligations under Regulation (EU) 2024/1689.
Research interests include AI system classification under the EU AI Act, the MDR × AI Act × MDCG 2025-6/9/10 layered architecture for medical AI manufacturers, deployer obligations and responsibility distribution across the AI value chain, regulatory asymmetries and downstream risk propagation, and the disconnect between governance frameworks and actual regulatory applicability.
Founder of Sprinkling Act, an independent pre-conformity advisory firm developing analyses and frameworks that reduce information asymmetry in AI regulation.
ACADEMIC IDENTIFIERS
This page consolidates the canonical identifiers used across Zenodo, ORCID, and SSRN. Cite from any of these: they all resolve to the same author entity.
PUBLICATIONS
Published 22 April 2026
Independent Research Report · Zenodo · CC BY 4.0
APA citation
Shucrani, L. B. (2026). EU AI Act Readiness: A Structured Screening of 50 European AI Companies (April 2026). Zenodo. https://doi.org/10.5281/zenodo.19671328
Published 6 May 2026
Annex to the EU AI Act Readiness Report · Zenodo · CC BY 4.0
APA citation · Annex A
Shucrani, L. B. (2026). EU AI Act Readiness — Annex A: The Deployer Multiplier (May 2026) (1.0). Sprinkling Act. https://doi.org/10.5281/zenodo.20042174
Published 22 May 2026
Discussion Paper · Sprinkling Act DP-2026-001 · Zenodo · CC BY 4.0
APA citation · Discussion Paper
Shucrani, L. B. (2026). The AI Act as a Third Structural Pole: A Constrained Retrospective on the Predictive Frame (2015 to 2026). Sprinkling Act Discussion Paper DP-2026-001. Zenodo. https://doi.org/10.5281/zenodo.20343243
RECOGNITION
Annex A (May 2026) is referenced in independent third-party sector intelligence. See Cited in section
OPEN INFRASTRUCTURE
The methodology and the tooling are public, so any reader, reviewer, or downstream tool can audit the logic, not just trust it.
Rule-based, no LLM · MIT
Generates signed, timestamped boundary attestations for the EU AI Act. Classifies a system's position against named gates (Art. 50, Art. 51, Annex III) as IN_SCOPE or UNDETERMINED, never as a compliance verdict. Emits a W3C Verifiable Credential, signed Ed25519 and timestamped via RFC 3161.
Framework · v1.3 · MIT
The six sequential regulatory gates that classify an AI system under Regulation (EU) 2024/1689. Documented openly so the classification logic is reproducible and auditable.
OpenTimestamps proofs
Cryptographic timestamp proofs for published Sprinkling Act reports. Each report is sealed on the Bitcoin blockchain, so its publication date is independently checkable.
ENTITY
Disclaimer
All Sprinkling Act research is independent. Reports are based exclusively on publicly available information. Classifications are estimates: only a personalised screening can confirm definitive regulatory status. This page is informational and does not constitute legal advice. Sprinkling Act is not a law firm, not a Notified Body, not a certification body, and not affiliated with the European Commission, the European Parliament, or any national supervisory authority. It operates as an independent pre-conformity advisory firm.