Detecting concept drift in high-velocity event streams is critical for maintaining the reliability of process monitoring systems. However, traditional approaches often rely on rigid statistical tests or handcrafted features, struggling to capture complex, evolving process behaviors. To address this limitation, this paper proposes a novel adversarial representation learning framework for adaptive online drift detection. The proposed framework leverages a Generative Adversarial Network (GAN) where the discriminator functions as a dynamic anomaly scorer, capable of identifying distributional shifts without explicit supervision. Crucially, to mitigate catastrophic forgetting during model updates, the framework incorporates an experience replay mechanism that retains a buffer of recent normal traces. Extensive experiments on eight synthetic datasets demonstrate that this approach achieves near-perfect recall and reduces detection latency by over 50% compared to the state-of-the-art PrefixCDD baseline. Furthermore, evaluations on the real world BPI Challenge 2017 log highlight the model’s robustness to noise and irregular human behaviors, yielding significant improvements in precision and F1-score while maintaining real-time efficiency.