# Knowledge Sharing in Security-Sensitive Communities
## 6 Discussion  
Intelligence organizations are responsible for defining critical information, collecting it, and evaluating and distributing it to the relevant parties. However, information and knowledge sharing occur alongside confidentiality and compartmentalization regulations [2]. Sharing takes place when network members trust each other and so in a network structure, trust may be quantified and assessed using Social Network Analysis measures. This paper investigates differences in social network measures between group discussions involving military and civilian intelligence personnel (security-sensitive) and group discussions that include members from diverse organizations, such as industry and academia (non-security-sensitive). Trust is fundamental to all aspects of intelligence work [83]. In international intelligence organizations, social ties and trustworthiness perceptions are more influential than often assumed, with shared professional standards and traits enabling collaboration [84]. However, trust remains a complex multifaceted phenomenon influenced by various factors, including human interactions with AI systems and end-user involvement in development [85]. The current study compared the cultural diversity of sharing and trust, based on previous research that presented the impact of cultural diversity on knowledge sharing [4]--the results of our study confirmed the cultural impact of sharing and trust on information sharing. In addition, previous studies addressed the effect of the trust dimension on human behavior and information sharing, which were reflected in our results [5]. The method we used, discourse analysis by representing it in a communication graph and using SNA methods, is taken from the literature [6]. The social network measures of this study indicate that knowledge sharing depends on trust, as evidenced by the current outcomes of Social Network Analysis in 18 instant messaging groups discussing COVID-19. Moreover, the results show that trust and the resulting knowledge sharing depend on the number of organizations represented in a group and their inclination toward information \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Mean Rank**} & \multicolumn{2}{c}{**Mann–Whitney U Test**} & \multicolumn{1}{c}{\(p\)} \\ \hline \multirow{2}{*}{**Network Attribute**} & **Heterogeneous Network** & \multicolumn{2}{c}{**Homogenous**} & & \\  & **(N = 14)** & **Security-Sensitive (N = 4)** & & \\ \hline Reciprocity & 7.45 & 12.71 & \(Z=-2.038\) & \(p=0.042\) \\ Diameter & 11.64 & 6.14 & \(Z=-2.255\) & \(p=0.024\) \\ Radius & 12.18 & 5.29 & \(Z=-3.082\) & \(p=0.002\) \\ Density & 7.45 & 12.71 & \(Z=-2.038\) & \(p=0.042\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Mann–Whitney U test for differences in network structures between networks. security. All our hypotheses were accepted, showing that knowledge flow is the result of a low number of organizations and having an organizational tradition of information security. In other words, trust and knowledge sharing develop over time in dense communities aided by low heterogeneity. This effect is interesting considering that the intelligence communities in the present study engaged in non-intelligence material as the topic was COVID-19. Possibly, knowledge sharing is enhanced as a result of an organizational requirement that creates a \"habit of sharing\" that carries over to non-security related topics such as the current example of a health-related topic. The same \"habit of sharing\" does not support sharing when additional non-intelligence organizations join the conversation. In this case, the \"habit of secrecy\" becomes dominant. Knowledge sharing in homogeneous communities faces challenges despite the potential benefits. While homogeneous communities with shared concerns facilitate intra-community knowledge exchange, sharing between them is more complex due to a lack of shared identity and trust [86]. Online communities, though seemingly boundary-free, still exhibit preferences for sharing with similar others.