Metaverse-Based Business Models (MBMs) and Shadow AI Usage: The Solution to Knowledge Leakage in Business Industry

Abstract

This study examined the role of artificial intelligence (AI) governance frameworks, approved AI solutions, AI risk management, monitoring tools, and transparent AI policies in reducing knowledge leakage among business organizations while managing Shadow AI solutions within Metaverse-Based Business Models (MBMs). Utilizing a quantitative research approach, data was collected through a structured questionnaire survey from business professionals engaged in AI-driven MBMs. A statistical analysis tool was employed to assess the relationships between key governance mechanisms and knowledge leakage mitigation. The results validate the hypothesis that AI governance frameworks significantly decrease knowledge leakage through policies and accountability structures. AI solutions will only be approved to prevent unauthorized use of AI and, consequently, reduce security risks. Risk assessments and audits as part of risk management strategies for AI are important processes to identify vulnerabilities in such AI-driven MBMs. Moreover, AI monitoring tools help monitor data security by identifying unauthorized AI applications in real time. Ensuring a better environment to operate in by boosting compliance and ethical AI adoption by transparent AI policies. There is need for policymakers to craft standardized AI regulations and for technology developers to create secure and transparent AI solutions. This study offers important insights for businesses, practitioners, and researchers aiming to stimulate AI-enhanced security and compliance in MBMs.