Ensuring process compliance is critical for companies and organizations to avoid regulatory penalties, financial losses, and reputational damage. As a result, research has focused on detecting non-compliant instances where processes deviate from rules imposed on their execution and formalizing legal documents to enable automatic compliance verification. However, a crucial but overlooked phenomenon is overcompliance, where processes exceed what is required by regulations or policies, potentially leading to inefficiencies, increased operational costs, or overly rigid processes. While the reasons why companies overcomply with regulations have been extensively analyzed in environmental economics, overcompliance has been largely disregarded in process compliance, where compliance is often categorized as either violated or satisfied, and, at most, the degree of undercompliance is considered. We propose a formal framework for evaluating a degree of compliance, which specifically highlights the differences between perfect compliance, (maximal) undercompliance, and (maximal) overcompliance. Furthermore, we present a technique for estimating the costs associated with over and undercompliance. The approach is prototypically implemented and evaluated using real-world and synthetic event logs.