:The increasing complexity and intensity of cyber threats require a paradigm shift in the approach to traditional reactive security frameworks towards proactive defense frameworks. The paper explores the disruptive potential of artificial intelligence within modern cybersecurity systems, and how it can be used to provide predictive threat intelligence and support proactive defense capabilities, anticipating and preempting emerging threats.The use of advanced computational algorithms, such as machine learning models, deep learning models, and natural language processing, allows AI systems to handle large, heterogeneous datasets within seconds, allowing detection of unusual behaviors patterns, prediction of possible attack vectors, and automated response strategies. The strategic adoption of AI technologies significantly improve the accuracy of threat detection, reduce false positive rates, and improve incident response time, especially in complex cloud networks and new network architectures, including 5G and 6G systems.Nevertheless, AI-based cybersecurity deployment also presents numerous threats which should be carefully viewed, such as the consequences of data privacy, the possibility of algorithmic bias, the lack of interpretability of models, and vulnerability to adversarial examples. In this study, the authors introduce a detailed overview of AI usage in cybersecurity, a conceptualization of their application, the assessment of its functionality, and the critical analysis of the related ethical and technical dilemmas.This paper ends with the formulation of policy recommendations and outlines of the future research directions that need to be pursued to develop a more robust and reliable cybersecurity ecosystem. The results highlight the extreme significance of the realization of synergy between human skills and smart automation systems in the security of digital resources and infrastructure.