Assessing the firm value of a company operating in the information security industry presents challenges, mainly due to the complexity of estimating the business value of information security products. Relying solely on the economic value of these information security products may lead to inaccuracies in evaluating the firm's financial performance. To address this issue, we propose an alternative method that focuses on analyzing the information security firm's strategic dynamics over time. This proposed approach takes into account the dynamics of the information security technology industry. Our central argument is that the value of an information security firm is closely tied to its ability to adapt persistently and swiftly to emerging information security demands. To measure these strategic dynamics, we employ machine learning techniques. In our study, we examine the relationship between the strategic dynamics of information security firms and their capital and sales market firm value. To do so, we utilize a panel dataset and employ both machine learning and econometric approaches for analysis. By adopting this methodology, we aim to provide a more accurate and comprehensive understanding of the financial value of information security firms in the industry. In this research, we employ publicly available data from sources like WRDS, USPTO, LexisNexis database, encompassing capital market information, news, and magazine articles from December 2005 to January 2017.