AI, machine learning and deep learning in cyber risk management

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in both climate change and cyber risk management has significantly bolstered their effectiveness. These advanced technologies enable organizations to handle extensive datasets, whether related to climate patterns or cyber activities, with greater efficiency, leading to more informed decision-making. In cybersecurity, AI and ML are utilized to predict potential threats, automate tasks, and manage cyber risks. However, challenges persist in ensuring the accuracy of information, understanding the functionality of models, and protecting individuals’ data. Ethical use of personal information and safeguarding privacy remain critical, with researchers needing to prioritize accuracy, transparency in AI models, and fairness in decision-making. Emerging technologies like edge computing, federated learning, explainable AI, and quantum computing are revolutionizing risk management and digital security. These advancements offer opportunities for better understanding risks, identifying threats more rapidly, and improving decision-making. Yet, issues such as data accuracy, model interpretability, and privacy concerns must be resolved to fully address cyber risks. This paper significantly contributes to understanding the role of AI, DL, and ML in cyber risk management by providing a thorough review of the literature and a discussion on both the challenges and future trends in the field. This paper explores AI, DL, and ML in the cyberrisk management through state-of-the-art analysis and future recommendations for further research. It discusses AI’s role in enhancing risk management, advancements in AI technologies, and their impact on traditional frameworks. The paper also highlights opportunities, challenges, and strategic and regulatory implications of AI in cybersecurity, concluding with future research directions.