Traditional cybersecurity systems that rely on static rule sets and signature-based detection all reach the same conclusion: dealing with new and more sophisticated threats in a continuously changing cyber security environment is far from simple. It causes substantial false positive rates and detection delays. The paper presents a complex data infrastructure security method that combines artificial intelligence (AI)-based models, machine learning, and real-time anomaly detection. In that instance, the proposed solution overcomes the primary difficulties with standard systems by using algorithms for detecting anomalies (such as Isolation Forest) and predictive modeling techniques (Gradient Boosting Machines -GBM-, Neuronal Networks,). The most significant additions are the Imperial Kernel for feature extraction and the PCA to automatically include reactions. The system has a substantially greater false positive rate and poorer detection accuracies than the proposed method, yielding 95% true positives and 93% true negatives. It also routinely outperforms the present system, with an average reaction time of three seconds and confinement times of less than one minute. Such enhancements have shown how quickly and efficiently the system detects such attacks, representing a significant advancement in cybersecurity procedures.