Artificial Intelligence Risk Management: A Comprehensive Framework for Organizational Implementation

Abstract

The rapid proliferation of artificial intelligence technologies across organizational contexts has generated unprecedented opportunities alongside substantial risks that demand systematic management approaches. This article presents a comprehensive analysis of AI-related risks and develops an integrated framework for organizational risk management. Drawing upon interdisciplinary scholarship spanning computer science, management, law, and ethics, the analysis identifies and categorizes the multifaceted risks associated with AI deployment, including technical risks such as algorithmic bias, opacity, and security vulnerabilities; organizational risks encompassing operational disruptions, strategic misalignment, and governance challenges; and broader societal risks involving ethical concerns, systemic effects, and environmental impacts. The article critically examines existing governance frameworks at international, national, and organizational levels, evaluating their effectiveness in addressing the distinctive characteristics of AI systems. Furthermore, it proposes evidence-based mitigation strategies that organizations can implement across the AI lifecycle, from conception through deployment and ongoing operation. The framework emphasizes the importance of contextual adaptation, recognizing that effective AI risk management must account for sector-specific considerations, organizational maturity, and stakeholder expectations. By synthesizing theoretical insights with practical guidance, this article contributes to the growing body of knowledge supporting responsible AI adoption while enabling organizations to realize the transformative potential of these technologies.