Process mining plays a crucial role in understanding and optimizing organizational workflows by leveraging event log data that captures the historical dynamics of business processes. However, the complexity of business process dynamics makes them challenging to analyze, even with graph models automatically extracted from historical data analysis. This complexity is exacerbated by recent trends in organizing event data. This paper addresses these challenges by proposing a method for extracting declarative knowledge based on sequential association rules, optimized for the newly proposed standard of object-centric event logs, and illustrating the interpretation of the discovered rules through various examples. The effectiveness of the method is demonstrated through an experiment using real-world data from a financial institution’s loan application process. The results reveal situations that require in-depth study, which can support compliance verification tasks as well as process monitoring and optimization efforts.