Survival analysis in medicine requires models that are both accurate and interpretable, yet deep survival models are often treated as black boxes. CoxKAN introduces a Cox proportional hazards Kolmogorov-Arnold Network that combines strong predictive performance with transparent functional structure. Evaluations on synthetic and real-world datasets show that it offers a practical balance between interpretability and high-performance survival modeling.