Artificial Intelligence-Driven Cybersecurity Framework Using Machine Learning for Advanced Threat Detection and Prevention

The escalating complexity, frequency, and diversity of cyber threats in today's hyper-connected digital landscape have rendered traditional security frameworks insufficient. In response, this research introduces a comprehensive, Al-driven cybersecurity architecture underpinned by state-of-the-art machine learning (ML) algorithms and the Artificial Neural Network-Interpretive Structural Modeling (ANN-ISM) paradigm. The proposed system is engineered to deliver real time threat detection, advanced vulnerability assessment, intelligent risk response, and scalable threat mitigation capabilities. This study adopts a multi-dimensional methodology involving a systematic literature review, empirical validation through industry-level surveys, and a case-based evaluation of insecure coding practices. Central to this framework is the integration of supervised, unsupervised, and reinforcement learning for adaptive anomaly detection and adversarial threat resilience. Furthermore, the incorporation of federated learning offers decentralized, privacy preserving threat intelligence, while Explainable AI (XΑΙ) modules ensure transparency and trust in decision-making. To operationalize the model, we classify cybersecurity maturity levels and establish a multi-layered response mechanism tailored to evolving organizational needs. The results of the implemented framework demonstrate significant improvements over traditional systems in terms of predictive accuracy, response time, and adaptability to emerging threats. By aligning Al innovations with real-world software development practices and adversarial defense strategies, this research provides a forward-looking foundation for building scalable, intelligent, and sustainable cybersecurity infrastructures.