Cyber security systems have to include computational intelligence as their increasing complexity calls for. The transformation of digital security by artificial intelligence (AI), machine learning (ML), and deep learning (DL) is investigated in this work. Cybersecurity resilience has been much enhanced by new artificial intelligence-driven solutions including automated threat response systems, real-time anomaly detection, and intelligent intrusion detection systems. Our results show that compared to conventional techniques, AI-enhanced security systems lower false positives by 25% and detect threats 30% faster. The originality of this work is in its thorough assessment of state-of-the-art (SOTA) cybersecurity models and suggestion of an enhanced adaptive threat intelligence system to raise detection rates. This research offers a comparative analysis of current models, highlighting their advantages and disadvantages through the analysis of results and error metrics. This study offers by examining empirical results and error metrics, this study opens new avenues for developing reliable AI-powered cybersecurity solutions.