Collaborative information systems (CISs) allow groups of users to communicate and cooperate over common tasks. CIS are increasingly relied upon to manage sensitive information. In this paper, we introduce a framework to detect anomalous insiders from the access logs of a CIS by leveraging the relational nature of system users as well as the meta-information of the subjects accessed. The framework is called the community anomaly detection system (CADS). CADS consist of two components: 1) relational pattern extraction, which derives community structures and 2) anomaly prediction, which leverages a statistical model to determine when users have sufficiently deviated from communities.