Evolving AI Governance Risk Assessment Methodologies

This white paper presents a practitioner's perspective on the evolution of AI governance risk assessment methodologies, emphasizing the need to transition from qualitative to quantitative approaches. Authored by Donna Gallaher, a recognized authority in cyber risk management, the document outlines the challenges posed by outdated risk assessment frameworks, particularly those based on subjective methodologies. It discusses the implications of these frameworks on organizational efficiency and decision-making, highlighting how they can lead to significant business harm and competitive disadvantages. The paper critiques the current AI governance certification process, which relies on qualitative assessments that may not align with the practical needs of organizations. It stresses the importance of adopting quantitative methodologies, such as the FAIR model, to enhance the effectiveness of AI governance and ensure compliance with regulatory expectations. The author shares personal insights from her experiences navigating the certification process, underscoring the critical need for a shift in professional education to better prepare practitioners for the complexities of AI governance.