One of the most difficult challenges facing network operators is to estimate risk and allocate resources in adversarial environments. Failure to properly allocate resources leads to failed activities, poor utilization, and insecure environments. In this paper, we explore an optimization-based approach to allocating resources called a mission-oriented security model. This model integrates security risk, cost and payout metrics to optimally allocate constrained secure resources to discrete actions called missions. We model this operation as a Mixed Integer Linear Program (MILP) which can be solved efficiently by different optimization solvers such as MATLAB MILP solver, IBM-CPLEX optimizer or CVX solver. We further introduce and explore a novel method to evaluate security risk in resource planning using two datasets—the Ponemon Institute cost of breach survey and CSI/FBI surveys of security events. Data driven simulations are used to validate the model robustness and uncover a number of insights on the importance of risk valuation in resource allocation.