AI Deployment Authorisation: A Global Standard for Machine-Readable Governance of High-Risk Artificial Intelligence

TL;DR

This paper introduces ADAS, a machine-readable framework for legally and economically grounded AI deployment authorization, enabling enforceable, verifiable deployment decisions aligned with regulations like the EU AI Act.

Contribution

It presents the formal design and implementation of ADAS, a novel cryptographically verifiable deployment certificate system for AI governance.

Findings

Operationalizes EU AI Act and US infrastructure regulations

Provides cryptographically verifiable deployment certificates

Enables scalable, lawful AI deployment decisions

Abstract

Modern artificial intelligence governance lacks a formal, enforceable mechanism for determining whether a given AI system is legally permitted to operate in a specific domain and jurisdiction. Existing tools such as model cards, audits, and benchmark evaluations provide descriptive information about model behavior and training data but do not produce binding deployment decisions with legal or financial force. This paper introduces the AI Deployment Authorisation Score (ADAS), a machine-readable regulatory framework that evaluates AI systems across five legally and economically grounded dimensions: risk, alignment, externality, control, and auditability. ADAS produces a cryptographically verifiable deployment certificate that regulators, insurers, and infrastructure operators can consume as a license to operate, using public-key verification and transparency mechanisms adapted from secure software supply chain and certificate transparency systems. The paper presents the formal specification, decision logic, evidence model, and policy architecture of ADAS and demonstrates how it operationalizes the European Union Artificial Intelligence Act, United States critical infrastructure governance, and insurance underwriting requirements by compiling statutory and regulatory obligations into machine-executable deployment gates. We argue that deployment-level authorization, rather than model-level evaluation, constitutes the missing institutional layer required for safe, lawful, and economically scalable artificial intelligence.