Public Sector AI Deployments Face Cyber Governance Failures

The intersection of AI adoption, cybersecurity governance, and public sector institutional constraints has been an under-examined area. Existing literature often addresses these topics in isolation, failing to explain how AI specifically leads to cybersecurity governance failures within government organizations or to test current governance instruments against AI-specific public sector challenges. This research bridges that gap by proposing a seven-domain typology that identifies ten specific AI-driven cyber governance failure causes, grounded in an analysis of public sector institutions. It also presents a three-pathway failure model, illustrating how accountability, operational resilience, and compliance failures interact and reinforce each other in the context of AI deployment. The paper includes a structured coverage matrix, evaluating five major governance frameworks (NIST CSF 2.0, ISO/IEC 27001, COBIT, NIST AI RMF, and ISO/IEC 42001) against its typology. The findings indicate that no single instrument adequately addresses critical issues like "Shadow AI," "speed asymmetry," or "governance vacuum" with the operational specificity required for public sector application. The concept of "speed asymmetry" is introduced as a new structural construct with a defined mechanism, and the overall framework provides a design specification for an AI-enabled cybersecurity maturity model tailored for government organizations.