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.

This study examines the phenomenon of disinformation as a threat in the realm of cybersecurity. We have analyzed multiple authoritative cybersecurity standards, manuals, handbooks, and literary works. We present the unanimous meaning and construct of the term cyber threat. Our results reveal that although their definitions are mostly consistent, most of them lack the inclusion of disinformation in their list/glossary of cyber threats. We then proceeded to dissect the phenomenon of disinformation through the lens of cyber threat epistemology; it displays the presence of the necessary elements required (i.e., threat agent, attack vector, target, impact, defense) for its appropriate classification. To conjunct this, we have also included an in-depth comparative analysis of disinformation and its similar nature and characteristics with the prevailing and existing cyber threats. We, therefore, argue for its recommendation as an official and actual cyber threat. The significance of this paper, beyond the taxonomical correction it recommends, rests in the hope that it influences future policies and regulations in combatting disinformation and its propaganda.

Disinformation or "fake news" has continuously proven to be a pervasive threat in the digital space. The spread and persistence of disinformation especially in the social networking media sites currently factors as one of the most challenging threat for users and content administrators alike. Its ecosystem encompasses several attributing factors including but not limited to humans as information users and source, social communication model as its channel, current trust models in place as defense and guards against it, and finally the archival correction that may halt its persistence in the social space. In this paper, the author aims to explore the dynamics of the several interacting fields i.e. Psychology and Computer Science, their influence on its phenomenon, which provides an ideal interdisciplinary and holistic approach to its reduction and management. Another equally important section in this paper is its attempt to advocate to formally recognize disinformation as a cybersecurity threat for its prospective future categorization. The possible application of discourse analysis as a potential technological tool for its detection as solution is also discussed.

This paper explores the relationship between a person's demographic data, age (IV1), gender (IV2), ethnicity (IV3), and political ideology (IV4), and the risk of him/her falling prey to Mis/Disinformation attacks (DV). Participants ( $\mathrm{n}=161$ ) were subjected to the Fake News and deepfake test (15-item). The main data analysis tool employed by this study is multiple linear regression. Important findings of the study include the revelation of the disparity in the performance of the subjects from the underrepresented groups against those who are not. This paper further confirms that an increase in age is a risk for being Disinformed. The predictive model further reveals the profile of a most likely Disinformation victim. The intended target audiences of this paper are policymakers, social scientists, and tech companies.

This paper explores the relationship between a person's outlook on the issues of freedom of speech (IV1), fake news regulation (IV2), and the risk of him/her falling prey to Mis/Disinformation attacks (DV). Participants (n=162) were explicitly asked to choose a position among these issues and were subjected to the Fake News and deepfake test (15-item). The main data analysis tool employed for this study is factorial, two-way ANOVA. Important findings of the study include the revelation of the disparity in the performance of the subjects who are leaning to the government for information regulation against those who prefer the tech companies, among others. The intended target audience of this paper are policymakers and legal professionals possibly seeking for judicial references.

To further reduce the pervasiveness of fake news on social networking sites and increase content integrity, an important yet one of the most basic of defenses has be explored—content descriptors. This paper investigates the effects of content descriptors in aiding the human factor against disinformation attacks through its recognition. An explicit fake news test was employed to assess the users on how they use these descriptors as contextual clues in categorizing specific contents as either legitimate or misleading. The results of this study reveal the varying effect of contextual clues in a population to recognize fake news.