<p>The rapid expansion of cloud computing has significantly increased the scale, sophistication, and frequency of cyber threats, making reliable intrusion detection essential for secure cloud environments. However, conventional single-stage intrusion detection systems exhibit high false-positive rates, poor probability calibration, and degraded performance under severe class imbalance, thereby limiting their real-world deployment. This paper proposes a novel two-stage Generative-AI fusion intrusion detection model that integrates discriminative and generative learning to enhance predictive reliability and decision confidence. In the first stage, a binary classifier combining a Feature-Token Transformer and a Variational Autoencoder differentiates normal from malicious traffic using class-balanced focal loss and a precision-oriented routing threshold to minimize false alarms while preserving detection sensitivity. In the second stage, detected attack samples are forwarded to a conditional multi-class classifier for fine-grained attack categorization, incorporating SMOTE-based rebalancing, class-balanced focal loss, and class-specific threshold optimization to improve minority-class detection. The post-hoc temperature scaling is applied at both stages to produce well-calibrated probability estimates and support risk-aware decision-making. The performance of the proposed model measured on the UNSW-NB15 and NSL-KDD benchmark datasets indicate that proposed model performed well as compared to the single-stage Generative-AI fusion and evolutionary feature-selection baselines model and obtained the accuracy score of 94.03% and 77.07%, respectively to delivered the improved calibration and reduced false-alarm, and provide the robust and deployment-ready solution for modern cloud security.</p>