Threat evaluation is a vital process in air and missile defense systems. The reliable output of threat evaluation is essential in order to use sensor and weapon resources efficiently. Fuzzy rule-based system has been proven to be effective and widely used in threat evaluation decision support systems in air defense applications since it can model expert human knowledge. Creating this fuzzy rule-based system that reflects user behavior requires constructing fuzzy rules increasing exponentially with the number of fuzzy variables and the membership functions and subsequently manually tuning according to expected results. Building such a system is not only time-consuming but also difficult to adapt to changes of threat behavior in different operational contexts. In this paper, a methodology for automated generation and tuning of the fuzzy rule-based system has been proposed starting from a survey applied to air defense field experts to context-dependent near real-time adaptive tuning via interaction with the user during operation. The applicability of the proposed method is presented with numerical performance results using a survey applied to a group of air defense operators. Additionally, the effect of the context-dependent adaptive fuzzy rule-based tuning process is explored for different operational contexts.