Active Membership Inference Test (aMINT) is a method designed to detect if given data was used during the training of machine learning models. In Active MINT, we propose a novel multi-task learning process that involves training simultaneously two models: the original or Audited Model, and a secondary model, referred to as the MINT Model, responsible for identifying the data used for training the Audited Model. This novel multi-task learning approach has been designed to incorporate the auditability of the model as an optimization objective during the training process of neural networks. The proposed approach incorporates intermediate activation maps as inputs to MINT layers, which are trained to enhance the detection of the training data. We present results using a wide range of neural networks, from lighter architectures like MobileNet to more complex ones such as Vision Transformers, evaluated across 5 public benchmarks. Our proposed Active MINT achieves over 80% accuracy in detecting if given data was used for training, significantly outperforming previous approaches in the literature. Our proposed aMINT and related methodological developments contribute to increasing transparency in AI training, therefore facilitating stronger safeguards in AI deployments in order to achieve proper security, privacy, and copyright protection (Code will be available in https://github.com/Anonymized).