Cybersecurity is a concern for organizations in this era. However, strengthening the security of an organization’s internal network may not be sufficient since modern organizations depend on third parties, and these dependencies may open new attack paths to cybercriminals. Cyber Third-Party Risk Management (C-TPRM) is a relatively new concept in the business world. All vendors or partners possess a potential security vulnerability and threat. Even if an organization has the best cybersecurity practice, its data, customers, and reputation may be at risk because of a third party. Organizations seek effective and efficient methods to assess their partners’ cybersecurity risks. In addition to intrusive methods to assess an organization’s cybersecurity risks, such as penetration testing, non-intrusive methods are emerging to conduct C-TPRM more easily by synthesizing the publicly available information without requiring any involvement of the subject organization. In this study, the existing methods for C-TPRM built by different companies are presented and compared to discover the commonly used indicators and criteria for the assessments. Additionally, the results of different methods assessing the cybersecurity risks of a specific organization were compared to examine reliability and consistency. The results showed that even if there is a similarity among the results, the provided security scores do not entirely converge.

Artificial intelligence (AI) methods have revolutionized and redefined the landscape of data analysis in business, healthcare, and technology. These methods have innovated the applied mathematics, computer science, and engineering fields and are showing considerable potential for risk science, especially in the disaster risk domain. The disaster risk field has yet to define itself as a necessary application domain for AI implementation by defining how to responsibly balance AI and disaster risk. (1) How is AI being used for disaster risk applications; and how are these applications addressing the principles and assumptions of risk science, (2) What are the benefits of AI being used for risk applications; and what are the benefits of applying risk principles and assumptions for AI‐based applications, (3) What are the synergies between AI and risk science applications, and (4) What are the characteristics of effective use of fundamental risk principles and assumptions for AI‐based applications? This study develops and disseminates an online survey questionnaire that leverages expertise from risk and AI professionals to identify the most important characteristics related to AI and risk, then presents a framework for gauging how AI and disaster risk can be balanced. This study is the first to develop a classification system for applying risk principles for AI‐based applications. This classification contributes to understanding of AI and risk by exploring how AI can be used to manage risk, how AI methods introduce new or additional risk, and whether fundamental risk principles and assumptions are sufficient for AI‐based applications.

The increasing reliance on digital and automated systems in the maritime industry has introduced significant cybersecurity risks to ship operations. While prior research has examined cyber risk management, systematic modeling of attack scenarios remains limited. This study addresses this gap by employing attack trees to systematically analyze potential cyberattack pathways against modern ships. Drawing insights from the literature and interviews with 10 experts, this research identifies key vulnerabilities in navigation, operational, and communication systems. The study outlines 20 attack scenarios across three primary objectives: crashing, capsizing, and immobilizing a ship. Expert validation emphasizes the real-world feasibility of these threats, with cyberattack scenarios involving crashing and immobilization deemed the most plausible, while capsizing was considered unlikely due to robust ship designs. The findings highlight the importance of strengthening network security, implementing redundancy measures, and enhancing crew training to mitigate cyber risks.in Expert feedback helped validate these insights, underscoring their relevance to real-world maritime operations. This research presents a systematic threat modeling framework that supports effective risk assessment and cybersecurity strategy development, offering a valuable contribution to the advancement of maritime cybersecurity practices.

There is growing interest in leveraging advanced analytics, including artificial intelligence (AI) and machine learning (ML), for disaster risk analysis (RA) applications. These emerging methods offer unprecedented abilities to assess risk in settings where threats can emerge and transform quickly by relying on “learning” through datasets. There is a need to understand these emerging methods in comparison to the more established set of risk assessment methods commonly used in practice. These existing methods are generally accepted by the risk community and are grounded in use across various risk application areas. The next frontier in RA with emerging methods is to develop insights for evaluating the compatibility of those risk methods with more recent advancements in AI/ML, particularly with consideration of usefulness, trust, explainability, and other factors. This article leverages inputs from RA and AI experts to investigate the compatibility of various risk assessment methods, including both established methods and an example of a commonly used AI‐based method for disaster RA applications. This article utilizes empirical evidence from expert perspectives to support key insights on those methods and the compatibility of those methods. This article will be of interest to researchers and practitioners in risk‐analytics disciplines who leverage AI/ML methods.