The field of cybersecurity and time series analysis is witnessing a significant shift towards causality-driven approaches. Researchers are moving away from traditional black-box models and focusing on developing methods that can uncover causal relationships and provide interpretable results. This is driven by the need to address the limitations of current models, such as lack of interpretability, adaptability to distribution shifts, and robustness under evolving system dynamics. The use of causal learning perspectives, such as causal graph profiling, multi-view fusion, and continual causal graph learning, is becoming increasingly popular. These methods have the potential to provide early warning signals, root cause attribution, and scalable adaptive causal frameworks. Noteworthy papers in this area include Rethinking Spatio-Temporal Anomaly Detection: A Vision for Causality-Driven Cybersecurity, which advocates for a causal learning perspective to advance anomaly detection in spatially distributed infrastructures, and Dynamic Sparse Causal-Attention Temporal Networks for Interpretable Causality Discovery in Multivariate Time Series, which introduces a novel architecture designed to enhance causal discovery by integrating dilated temporal convolutions and dynamic sparse attention mechanisms. Additionally, papers such as Efficient Discovery of Actual Causality with Uncertainty and Causality-informed Anomaly Detection in Partially Observable Sensor Networks are also making significant contributions to the field.