AI THREAT MODELLING IN LOCAL GOVERNMENT: ADVANCING CYBERSECURITY THROUGH REGULATORY FRAMEWORKS AND GOVERNANCE

Abstract

Local governments are no longer facing smart but unsophisticated threats but have become confronted by highly intelligent and automated threats because of fast-paced digitalization, aged IT infrastructure, and scarce cybersecurity talent. Smaller municipalities experience a larger number of incidents of AI (Artificial Intelligence) powered cyberattacks than larger governmental organizations but are incapable of leveraging the regulatory harmonization and technical capabilities for countering such threats. This paper advances a conceptual AI-enabled threat modelling architecture uniquely developed for the local government context by incorporating generative AI functionalities with current cybersecurity basic standards such as GDPR, NIST, and ISO 27001. Differing from typical threat modelling frameworks that have high false positives and are incapable of reflecting regulatory accountability, the introduced governance-compliant model utilizes AI for the automated detection of threats, generation of mitigation stories, and assistance for legal transparency needs. The research amalgamates global policy evolutionary work, cybersecurity governance texts, and ethical guidelines for AI to develop a regulatory-compliant architecture that increases explainability, accountability, and traceability of the cybersecurity operations for municipalities. By integrating AI threat detection efficiency with legal structures for compliance, the research advocates for a futuristic paradigm that enables local governments to realize cyber resilience achieving greater regulatory oversight and ethical certainty. The theoretical synthesis proposed in this paper uses generative AI in threat modeling in the areas of autonomous threat description, generation of mitigations, and compliance ready reporting. The proposed threat modeling approach is generalized and governance focused. It also conceptually demonstrates reduced false positives and improved accountability.