We extend continuous assurance research by proposing a novel continuous assurance architecture grounded in information fusion research. Existing continuous assurance architectures focus primarily on methods of monitoring assurance clients' systems to detect anomalous activities and have not addressed the question of how to process the detected anomalies. Consequently, actual implementations of these systems typically detect a large number of anomalies, with the resulting information overload leading to suboptimal decision making due to human information processing limitations. The proposed architecture addresses these issues by performing anomaly detection, aggregation, and evaluation. Within the proposed architecture, artifacts developed in prior continuous assurance, ontology, and artificial intelligence research are used to perform the detection, aggregation, and evaluation information fusion tasks. The architecture contributes to the academic continuous assurance literature and has implications for practitioners involved in the development of more robust and useful continuous assurance systems.

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