ICAIRE, International Center for AI Research and Ethics, under the auspices of UNESCO
All research

ICAIRE Ethical AI Series

ICAIRE Framework PaperICAIRE-EAI-2026-005

Risk-Based AI Assurance in the Saudi Public Sector

Aligning International Assurance Practices with the National AI Risk Management Framework

Nawaf Alsahmah, Ph.D.

Independent Researcher

Published October 2026

Cover of Risk-Based AI Assurance in the Saudi Public Sector

Abstract

Saudi Arabia has established a national foundation for managing artificial intelligence (AI) risk through the National AI Risk Management Framework, issued by the Saudi Data and Artificial Intelligence Authority (SDAIA) in 2026. The framework structures risk management around context and scope, risk identification, risk assessment, risk treatment, and monitoring and review. The policy challenge addressed in this paper is therefore not the absence of AI risk governance, but how outputs from this established process can be connected to a distinct assurance process that supports justified confidence that applicable requirements have been satisfied.

Governance and assurance

About the authors

Nawaf Alsahmah, Ph.D.

Independent researcher working on AI assurance and risk governance in the public sector.

Cite this research

Alsahmah, N. (2026). Risk-based AI assurance in the Saudi public sector: Aligning international assurance practices with the National AI Risk Management Framework (ICAIRE Framework Paper). International Center for Artificial Intelligence Research and Ethics. https://doi.org/10.68480/CEYS4608

BibTeX
@techreport{alsahmah2026assurance,  title       = {Risk-Based AI Assurance in the Saudi Public Sector: Aligning International Assurance Practices with the National AI Risk Management Framework},  author      = {Alsahmah, Nawaf},  institution = {International Center for Artificial Intelligence Research and Ethics},  type        = {ICAIRE Framework Paper},  number      = {ICAIRE-EAI-2026-005},  year        = {2026},  month       = oct,  doi         = {10.68480/CEYS4608},  url         = {https://doi.org/10.68480/CEYS4608}}