Washington D.C — December 27, 2025 — Rohan Sharma, award‑winning AI governance executive and CEO of Zenolabs.AI, today announced the filing of U.S. Provisional Patent Application No. 63/949,263 for an AI regulatory sandbox gatekeeping system designed to operationalize the EU AI Act’s regulatory sandbox framework and comparable regimes worldwide.
The patent‑pending invention introduces a computable compliance engine that ingests technical and legal artifacts from AI systems, scores them against the AI Act and related standards, and issues cryptographically signed digital eligibility tokens and standardized sandbox exit certificates.
Turning the EU AI Act sandbox into infrastructure
Under Article 57 of the EU AI Act, every Member State must stand up at least one AI regulatory sandbox by 2026 to let innovators test high‑risk AI systems under regulatory supervision. Yet regulators currently rely on manual document review, narrative self‑assessments, and ad‑hoc criteria that cannot scale to thousands of applicants.
Sharma’s patent‑pending system tackles this bottleneck by creating a structured Common Regulatory Data Model (CRDM) for sandbox applications, mapping model cards, risk assessments, and logs to specific EU AI Act provisions and companion frameworks such as NIST AI RMF and ISO/IEC 42001.
Sandbox Readiness Score and Liability Exposure Matrix
At the core of the invention is an automated scoring pipeline that generates:
- A Sandbox Readiness Score (SRS) that quantifies the technical maturity and compliance posture of an AI system against key Articles of the EU AI Act — including risk management, data governance, transparency, record‑keeping, and human oversight.
- A Liability Exposure Matrix (LEM) estimating cross‑regime exposure under the AI Act, GDPR, sectoral regulations, and emerging national AI laws, enabling regulators to prioritize high‑impact systems where sandbox participation can avert the most legal and societal harm.
By combining SRS and LEM, the platform ranks applicants in a liability‑weighted priority queue, surfacing “high‑risk, high‑maturity” systems for fast‑track admission while routing low‑maturity, low‑impact projects to alternative support channels.
Digital eligibility tokens and standardized exit certificates
Once an AI system crosses configurable readiness thresholds, the gatekeeping engine issues a cryptographically signed Digital Eligibility Token — a machine‑verifiable “boarding pass” that encodes the Sandbox Readiness Score, key compliance indicators, and a tamper‑evident audit trail.
Regulatory sandbox platforms can automatically verify these tokens via API, reducing manual intake friction and enabling cross‑border recognition of vetted applicants as contemplated by EU‑level coordination on sandboxes.
As sandbox testing progresses, the system continuously recalculates compliance scores and generates a Sandbox Exit Certificate aligned with ISO/IEC 42001 and mapped back to AI Act obligations, giving providers and supervisors a portable, machine‑readable record of risk reduction and design changes.
A new layer of supervisory technology (“SupTech”) for AI
“Regulators are under enormous pressure to stand up EU AI Act sandboxes that are fair, transparent, and scalable,” said Rohan Sharma, founder of Zenolabs.AI and widely recognized as a leading authority on AI governance and strategic AI export controls. “This patent is about giving supervisory authorities a quantitative gate — a shared language of readiness and liability — so that scarce oversight capacity flows to the AI systems that matter most.”
Sharma’s work builds on his prior patents in cryptographic AI governance, AI trust indices, and multi‑framework compliance engines, positioning the new sandbox gatekeeping system as the connective tissue between innovators, boards, and regulators. The platform is designed to integrate with national competent authorities, central banks, and sectoral regulators seeking to operationalize the AI Act alongside financial and data‑protection regimes.
Designed for EU, adaptable globally
While the invention is tightly aligned to the EU AI Act’s regulatory sandbox architecture, it is deliberately extensible to:
- UK, Canadian, and Gulf AI innovation sandboxes built on similar risk‑based experimentation models.
- Sector‑specific sandboxes in healthcare, financial services, critical infrastructure, and public sector AI.
- Multi‑jurisdictional pilots coordinated through the European AI Office and cross‑border regulatory consortia.
By encoding legal criteria as machine‑readable scoring rules, the system aims to become a reference infrastructure for AI SupTech, allowing regulators to explain and defend sandbox admission decisions while giving providers a clear roadmap to eligibility.
About Rohan Sharma
Rohan Sharma is CEO of Zenolabs.AI and an award‑winning AI governance and compliance leader whose work has been featured by the Institute of Directors, Yahoo Finance, and global policy forums. He advises boards, regulators, and multilateral institutions on responsible AI, export controls, and algorithmic accountability, and is the author of AI and the Boardroom, a Springer Nature title on board‑level oversight of AI.
Sharma’s portfolio includes patent‑pending innovations in cryptographic AI compliance, enterprise AI trust indices, and regulatory sandboxes for high‑risk AI, making him one of the most active inventors at the intersection of AI, law, and financial supervision.
Media and partnership enquiries
- Website: https://rohansharma.net
- AI governance advisory & partnerships: https://zenolabs.ai
- Media & speaking: [email protected]
For regulators, supervisors, and innovation hubs exploring EU AI Act regulatory sandboxes, Sharma is currently inviting select pilot partners to co‑design reference implementations of the gatekeeping engine with live datasets and multi‑framework scoring.