The aim of this book is to demonstrate the use of business‑driven risk assessments to address government regulations and guidelines specific to AI risks, as AI systems often require access to personal data. All aspects of AI, machine learning models, continuous learning, generalization, and predictive and descriptive analytics are dependent on massive datasets. The more diverse and comprehensive the data, the better an AI can perform. Therefore, AI systems require vast amounts of personal data, and should this data be accessed by unauthorized individuals or organizations, it will lead to a privacy breach, which may result in personal harm to citizens, i.e., identity theft. This book introduces the cyber risk investment model and the cybersecurity risk management framework used within business‑driven risk assessments to address government regulations, industry standards, and applicable laws. It can be used by various stakeholders who are involved in the implementation of cybersecurity measures to safeguard sensitive data. This framework facilitates an organization’s risk management decision‑making process to demonstrate the mechanisms in place to fund cybersecurity measures and demonstrates the application of the process by showcasing two case studies. Features: Aims to strengthen the reader’s understanding of industry governance, AI risk, and compliance practices. Incorporates an innovative approach to assess business risk management specific to AI systems. Explores the strategic decisions made by organizations when implementing cybersecurity measures and leverages an integrated approach to include risk management elements.
