Cybersecurity Threat Detection System

The rapid advancement of digital technologies has resulted in a significant increase in cyber threats, making conventional security mechanisms insufficient to handle modern and sophisticated cyberattacks. Artificial Intelligence (AI) has become a key enabler in strengthening cybersecurity by improving threat detection through machine learning, deep learning, and behavioral analysis techniques. AI-based security systems are capable of processing large volumes of data in real time to identify anomalies, anticipate potential attacks, and automate response actions. By utilizing AI, organizations can enhance their protection against emerging threats such as ransomware, phishing attacks, and advanced persistent threats (APTs). This paper explores the application of AI in cybersecurity with an emphasis on real-time threat detection, anomaly analysis, and predictive security models. It also discusses the benefits, limitations, and future directions of AI-driven cybersecurity solutions, highlighting the need to integrate AI with existing security frameworks to build a resilient defense system. The study indicates that AI-powered approaches significantly improve security effectiveness; however, challenges related to ethical issues, adversarial AI threats, and deployment complexity must be addressed to enable large-scale adoption.