Zero-Knowledge Audit Protocol

Verifiable Adherence to Formalized Rules Without Disclosure

The ZKAP methodology enables regulators to verify that AI systems adhere to formalized rules — whether derived from law (AI Act), regulation (GDPR), technical standards (ISO/IEC), ethics codes, or internal policies — without accessing proprietary models, trade secrets, or personal data.

Resolving the structural conflict between AI Act transparency, GDPR data protection, and trade secret law through cryptographic proof.

In practical terms: ZKAP attaches a cryptographic (zero-knowledge) proof to each AI decision, letting a regulator confirm the decision followed the rules in milliseconds — without access to the model, the training data, or any personal data. It is the one instrument that meets all four demands at once: continuity, non-disclosure, independence, and scale.

Radoslav Y. Radoslavov
Lead Methodologist in Legal Engineering • EU AI Attorney
Futurium
[9] Radoslavov, R.Y. (2026). “The Deferral Is the Admission: What the Digital Omnibus Tells Us About AI Act Enforceability.” Futurium — EU AI Alliance, 15 July 2026. 🔗 Read on Futurium
COLLAPSE
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PARENCY
[4] Radoslavov, R.Y. & Genov, M. (2026). The Collapse of Transparency: Cryptographic Regulation of AI in the Era of Opaque Algorithms. Book. Comparative analysis of EU, US, and Chinese regulatory models. 11 practical scenarios.
[7] Radoslavov, R.Y. (2026). “ZKAP — Public Information Hub.” GitHub repository, 22 April 2026. Includes README, publication list, citation metadata (CITATION.cff). 🔗 github.com/radoslavov-zkap/zkap-public
GS
[8] Radoslavov, R.Y. (2026). Google Scholar author profile. Verified email at radoslavov.bg. Research areas: Zero-Knowledge Proofs, AI Regulation, EU AI Act, Cryptographic Compliance, Legal Engineering. 🔗 scholar.google.com/citations?user=S2-iyH4AAAAJ
Zenodo
[6] Radoslavov, R.Y. (2026). “ZKAP: An Enforcement Protocol for Verifiable Regulatory Compliance of Machine-Learning Inference via Certified Stack Binding.” Zenodo preprint (embargoed until 31 March 2027; metadata public, full text released after embargo), 22 April 2026. 🔗 DOI 10.5281/zenodo.19698949
Futurium
[5] Radoslavov, R.Y. (2026). “After Mythos: Why Frontier AI Conformity Assessment Requires a Cryptographic Layer.” EU AI Alliance — European Commission Futurium, 18 April 2026. Read on Futurium
Futurium
[3] Radoslavov, R.Y. (2026). “ZKAP: Solving the Cognitive Barrier in AI Act & NIS2 Oversight.” EU AI Alliance — European Commission Futurium, 2 April 2026. Read on Futurium
AI Proceedings
[2] Radoslavov, R.Y. (2026). “Management and Regulation of AI Models in Public Administration: Cryptographic Transparency and Digitalization of Legal Norms.” Artificial Intelligence Proceedings, ISSN 3033-2923 (Print) / 3134-1667 (Online). Presented at XI Intl. Sci. Conf. “High Technologies. Business. Society”, Borovets, 23–26 March 2026, p. 75. 📄 Proceedings PDF | 🔗 DOI 10.5281/zenodo.19509511 | RGResearchGate
Industry 4.0
[1] Radoslavov, R.Y. (2025). “Management and Regulation of Artificial Intelligence Models: Concept for Transparency and Accountability in Administrative Activities.” Industry 4.0, Winter Session, Vol. 2, pp. 320–321, ISSN 2534-997X (Online) / 2535-0161 (Print). 🔗 DOI 10.5281/zenodo.19614243 | RGResearchGate

Strategic Enquiries

Maximilian Genov

Maximilian Genov

Head of Strategic Enquiries

+44 7460 801464

+44 7515 787014

zkap@advanced-consulting.london