Ethical and Secure Deployment of Generative AI: Balancing Innovation, Data Privacy, and Enterprise Risk Governance
Cornelia Ifeoma Ejoh
*
University of the District of Columbia, 4200 Connecticut Ave NW, Washington, DC 20008, United States.
Christopher Ugbong Akeke
Howard University, 2400 Sixth Street NW, Washington, DC 20059-0001, USA.
Oluseun Babatunde Oladoyinbo
Oyo State College of Agriculture and Technology, Igboora, Nigeria.
Onyii Henry
University of the District of Columbia, 4200 Connecticut Ave NW, Washington, DC 20008, United States.
Utin Nyimeobong Archibong
Liberty University, 1971 University Blvd, Lynchburg, VA 24515, USA.
*Author to whom correspondence should be addressed.
Abstract
This study examines the ethical and secure deployment of generative artificial intelligence in enterprise environments, with emphasis on innovation, data privacy, and risk governance. It addresses the gap between the rapid organisational adoption of generative AI systems and the slower development of institutional mechanisms for managing ethical, privacy, security, and regulatory risks. The study identifies the absence of a validated, integrated governance instrument that combines ethical, privacy, security, and enterprise risk controls for the specific characteristics of generative systems. A desk-based mixed-methods approach was used, combining systematic literature review, document analysis, thematic synthesis, comparative evaluation of governance frameworks, and Analytic Hierarchy Process weighting. Evidence was drawn from peer-reviewed literature and authoritative international governance instruments. Eight governance dimensions were assessed: scope and coverage, risk classification, data privacy, ethics and accountability, security controls, legal enforceability, enterprise applicability, and adaptability to generative AI. The findings show that existing governance instruments provide useful but fragmented coverage when applied independently. Ethics and accountability emerged as the highest-weighted dimension, followed by data privacy, security controls, and enterprise applicability. The proposed framework integrates five pillars: ethics and accountability, privacy and data governance, security governance, enterprise risk management, and innovation and compliance alignment. The study concludes that responsible enterprise deployment of generative AI requires coordinated, multi-layered governance rather than reliance on isolated ethical, technical, or legal controls.
Keywords: Generative artificial intelligence, enterprise governance, AI risk management, data privacy, responsible innovation, ethics and accountability, security governance, regulatory compliance, Analytic Hierarchy Process, governance effectiveness