Abstract
This paper explores the intersection of artificial intelligence (AI) adoption, cybersecurity governance, and public sector institutional constraints, addressing a gap in existing literature. Previous studies have typically treated AI cybersecurity risks, public sector governance, and framework adequacy independently, failing to integrate these streams to elucidate how AI adoption leads to cybersecurity governance failures in government organizations.
Contributions
The paper proposes a seven-domain typology identifying ten specific AI-driven cyber governance failure causes grounded in public sector institutional analysis. It presents a three-pathway failure model to show how accountability failure, operational resilience failure, and compliance failure interact and reinforce each other.
Framework Testing
A structured coverage matrix tests five major governance frameworks (NIST CSF 2.0, ISO/IEC 27001, COBIT, NIST AI RMF, and ISO/IEC 42001) against the typology. It finds that no instrument adequately addresses Shadow AI, speed asymmetry, or governance vacuum at the operational specificity required for public sector application.
Speed Asymmetry
The paper introduces speed asymmetry as a named structural construct with a specified mechanism, providing design specifications for an AI-enabled cybersecurity maturity model for government organizations.
Blogger's Review: This paper sheds light on the vulnerabilities of public sector organizations in the face of new technologies by integrating AI and cybersecurity governance. The concept of speed asymmetry warrants further exploration across other domains.