  Anomaly detection is of paramount importance in many real-world domains,
characterized by evolving behavior. Lifelong learning represents an emerging
trend, answering the need for machine learning models that continuously adapt
to new challenges in dynamic environments while retaining past knowledge.
However, limited efforts are dedicated to building foundations for lifelong
anomaly detection, which provides intrinsically different challenges compared
to the more widely explored classification setting. In this paper, we face this
issue by exploring, motivating, and discussing lifelong anomaly detection,
trying to build foundations for its wider adoption. First, we explain why
lifelong anomaly detection is relevant, defining challenges and opportunities
to design anomaly detection methods that deal with lifelong learning
complexities. Second, we characterize learning settings and a scenario
generation procedure that enables researchers to experiment with lifelong
anomaly detection using existing datasets. Third, we perform experiments with
popular anomaly detection methods on proposed lifelong scenarios, emphasizing
the gap in performance that could be gained with the adoption of lifelong
learning. Overall, we conclude that the adoption of lifelong anomaly detection
is important to design more robust models that provide a comprehensive view of
the environment, as well as simultaneous adaptation and knowledge retention.
