  This study offers an in-depth analysis of the application and implications of
the National Institute of Standards and Technology's AI Risk Management
Framework (NIST AI RMF) within the domain of surveillance technologies,
particularly facial recognition technology. Given the inherently high-risk and
consequential nature of facial recognition systems, our research emphasizes the
critical need for a structured approach to risk management in this sector. The
paper presents a detailed case study demonstrating the utility of the NIST AI
RMF in identifying and mitigating risks that might otherwise remain unnoticed
in these technologies. Our primary objective is to develop a comprehensive risk
management strategy that advances the practice of responsible AI utilization in
feasible, scalable ways. We propose a six-step process tailored to the specific
challenges of surveillance technology that aims to produce a more systematic
and effective risk management practice. This process emphasizes continual
assessment and improvement to facilitate companies in managing AI-related risks
more robustly and ensuring ethical and responsible deployment of AI systems.
Additionally, our analysis uncovers and discusses critical gaps in the current
framework of the NIST AI RMF, particularly concerning its application to
surveillance technologies. These insights contribute to the evolving discourse
on AI governance and risk management, highlighting areas for future refinement
and development in frameworks like the NIST AI RMF.
