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

Author(s)/Creator(s)

Daniel Djan Saparning, East Texas A&M UniversityFollow

Publication Title

SSRN

Document Type

Article

Abstract/Description

Modern artificial intelligence (AI) 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 approaches-such as model cards, audits, and benchmark evaluations provide descriptive information about model behaviour and training data but do not produce binding deployment decisions with legal or financial force. This paper introduces the AI Deployment Authorisation Score (ADAS). This machine-readable, regulator-grade framework evaluates AI systems across five legally and economically grounded dimensions: Risk, Alignment, Externality, Control, and Auditability, derived from safety engineering, alignment theory, algorithmic accountability, and liability economics. ADAS produces a cryptographically verifiable deployment certificate that regulators, insurers, and infrastructure operators can consume as a license to operate, using public-key infrastructure 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 operationalises the EU Artificial Intelligence Act, U.S. critical-infrastructure and cybersecurity governance, and insurance underwriting requirements by compiling statutory and regulatory obligations into machine-executable deployment gates. We argue that deployment-level authorisation, rather than model-level evaluation, constitutes the missing institutional layer required for safe, lawful, and economically scalable AI, aligning artificial intelligence with the same certification, liability, and insurance regimes that govern aircraft, medicines, and financial systems.

Department

College of Innovation and Design

Date

2026

Comments

Paper blueprint

Citation Information

Saparning, Daniel Djan, "AI Deployment Authorisation: A Global Standard for Machine-Readable Governance of High-Risk Artificial Intelligence" (2026). Student Publications. 1.
https://lair.etamu.edu/cid-student-publications/1