The rapid growth of digital technologies, cloud computing, and interconnected networks has significantly increased cybersecurity threats targeting enterprise systems and critical infrastructures. Traditional cybersecurity mechanisms often struggle to detect sophisticated attacks such as zero-day exploits, advanced persistent threats, ransomware, and insider attacks due to their reliance on static rule-based detection methods. Artificial Intelligence (AI) has emerged as a transformative technology capable of improving threat detection and automated response mechanisms in modern cybersecurity environments. This research explores AI-driven threat detection and response frameworks designed for advanced cybersecurity applications. The study examines the integration of machine learning, deep learning, natural language processing, and behavioral analytics within cybersecurity systems to identify malicious activities, predict emerging threats, and automate incident response procedures. AI-powered frameworks can analyze massive volumes of security data in real time, enabling faster detection of anomalies and minimizing human intervention during security operations. The research further investigates different AI models, detection algorithms, and intelligent response mechanisms used in enterprise cybersecurity infrastructures. Additionally, the study evaluates the advantages, limitations, and implementation challenges associated with AI-driven cybersecurity frameworks. The findings indicate that AI significantly enhances detection accuracy, threat intelligence, response speed, and overall security resilience. However, issues such as false positives, adversarial attacks, data privacy concerns, and high computational requirements remain major challenges requiring continuous research and optimization.
