Generative AI Risk Governance in Organizational Information Systems: A Conceptual Model Integrating Security, Ethics, and Digital Trust - Nurdiyanto Yusuf (Asosiasi Dosen Muda Indonesia, 2026)
The rapid adoption of Generative Artificial Intelligence in organizational information systems has created new opportunities for improving productivity, decision-making, service innovation, and knowledge management. However, its implementation also introduces critical risks related to data privacy, information security, inaccurate outputs, algorithmic bias, ethical misuse, and declining user trust. Objective: This study aims to develop a conceptual model of Generative AI risk governance by integrating AI governance readiness, information security control, ethical AI awareness, user digital trust, and AI adoption effectiveness. The model is proposed to explain how organizations can adopt Generative AI in a secure, ethical, responsible, and trusted manner. Methodology: This study employed a conceptual research design using an integrative literature review approach. Data were collected from secondary academic sources, including peer-reviewed journal articles, reputable conference proceedings, and official technical reports relevant to Generative AI, information systems, cybersecurity, AI ethics, responsible AI governance, and digital trust. The data were analyzed through thematic synthesis to identify conceptual domains, relationships among constructs, and research propositions. Findings: The findings indicate that AI governance readiness serves as a foundational construct that strengthens information security control and ethical AI awareness. These two mechanisms contribute to user digital trust, which subsequently supports the effectiveness of Generative AI adoption in organizational information systems. Implications: This study implies that organizations should not adopt Generative AI solely based on technological benefits. Organizations need to establish governance policies, security controls, ethical guidelines, user education, and trust-building strategies to ensure that Generative AI implementation is safe, accountable, and aligned with organizational objectives. Originality: The originality of this study lies in its integrated conceptual framework, which connects technology adoption, information security, AI ethics, responsible AI governance, and digital trust into a single model for responsible Generative AI implementation in organizational information systems.