This document proposes a process to detect outliers in application system audit logs using data mining techniques. It begins with an introduction to systems auditing and the importance of audit logs. Then it discusses related work using data mining for auditing and different clustering and outlier detection algorithms. The proposed process involves pre-processing data, applying LOF and DBSCAN clustering algorithms to detect outliers, combining the results, applying classification algorithms and rules to determine outlier types. An experiment on a university management database achieved over 66% efficacy and less than 1% false positives. The study concludes the process can help auditors by facilitating outlier detection in real databases.