Artificial intelligence (AI) is being adopted at an exponential rate to improve efficiency, decision-making, and cybersecurity, but its rapid integration introduces new and often poorly understood risks, including system errors, algorithmic bias, data privacy concerns, security vulnerabilities, and ethical dilemmas. This paper examines how organizations are implementing AI and evaluates the National Institute of Standards and Technology's AI Risk Management Framework (NIST AI RMF) as a tool for managing these risks. It reviews the benefits of AI adoption alongside the risks emerging from its use in business and broader society and examines the legal and ethical challenges organizations face when implementing AI risk management, including barriers specific to small and medium-sized enterprises (SMEs). Drawing on current literature and complementary regulatory frameworks, including the EU AI Act and ISO/IEC 42001, the paper situates the NIST AI RMF's four core functions, Govern, Map, Measure, and Manage, within this broader governance landscape. It finds that the primary obstacle to effective AI governance is not a lack of standards but inconsistent and incomplete organizational implementation. The paper concludes by identifying current gaps in AI governance practice and proposing future directions for improving AI risk management and security practices across organizations of varying size and maturity.
Kennedy et al. (Mon,) studied this question.