The landscape of security has been changed by the increase in online market places, and the rapid growth of mobile and wireless networks. Users are now exposed to greater risks as they interact anonymously in these domains. Despite the existing security paradigms, trust among users remains a problem. Reputation systems have now gained popularity because of their effectiveness in providing trusted interactions. We argue that managing reputation by relying on history alone and/or biased opinions is inadequate for security, because such an approach exposes the domain to vulnerabilities. Alternatively, the use of historical, recent and anticipated events supports effective reputation management. We investigate how the dynamic data-driven application systems paradigm can aid reputation management. We suggest the use of the paradigm's primitives, which includes the use of controller and simulation components for performing computations and predictions. We demonstrate how a dynamic framework can provide effective reputation management that is not influenced by biased observations. This is an online decision support system that can enable stakeholders make informed judgments. To highlight the framework's usefulness, we report on its predictive performance through an evaluation stage. Our results indicate that a dynamic data-driven approach can lead to effective reputation management in trust-reliant domains.