The rapid integration of Generative Artificial Intelligence (GenAI) into enterprise ecosystems has fundamentally transformed how organizations manage operations, cybersecurity processes, governance structures, and strategic decision-making. Technologies such as large language models (LLMs), AI copilots, intelligent automation systems, and conversational AI platforms are increasingly being deployed across finance, healthcare, education, cybersecurity, and digital governance domains. Despite these remarkable advancements, the uncontrolled adoption of GenAI has introduced a new and largely uncharted category of governance, compliance, and cybersecurity challenges that existing enterprise audit frameworks are ill-equipped to address. This study proposes a Governance-Centric AI Audit Framework (GAIAF) specifically designed for enterprise cybersecurity governance and compliance auditing in Generative AI ecosystems. The proposed framework integrates AI governance principles, continuous auditing mechanisms, cybersecurity intelligence, operational accountability, and regulatory compliance into unified five-layer enterprise architecture. The framework is aligned with internationally recognized standards including the NIST AI Risk Management Framework. Experimental evaluation conducted within a simulated enterprise environment demonstrates that governance-oriented AI auditing significantly improves compliance readiness (+53 percent), audit traceability (+51 percent), operational accountability (+52 percent), and cybersecurity visibility (+52 percent) compared to traditional audit models. The findings confirm that integrating continuous AI auditing with enterprise governance can substantially reduce the systemic risks associated with enterprise-scale AI deployment.
Sharma et al. (Wed,) studied this question.
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