From Rule-Based Systems to Artificial Intelligence: A Comparative Review of Modern Cyber Security Approaches

Cybersecurity has become a multifaceted, dynamic, and hostile problem because many critical systems and infrastructures were becoming digitized and transformed in response. Although the concept of artificial intelligence (AI) has become a potent tool in cyber defense enabling better anomaly detection, predicting threats, and automating responses, it equally brings forth novel threats through creation of more dynamic, evasive and scalable attacks. In this review, the author focuses on the emerging dual role of the AI in cybersecurity and the development of AI as the device that may be both defensive and a threat multiplier. The paper employs a systematic literature review approach to generalize findings of the recent empirical research, surveys, and models of the key cybersecurity aspects. It will analyze the AI-based anomaly detection, the threat detection using deep learning, and the automated defense, and critically evaluate the data quality, practice of evaluation, and reproducibility. The results indicate that anomaly detection is the heart of the AI-based cybersecurity infrastructures; nevertheless, the majority of existing technologies are based on simplistic threat models, unrealistic datasets, and accuracy focused performance metrics that prevent real-world applications. The review also finds structural gaps between research findings and operational needs with methodological weakness and overperformance assertions. To provide the comprehensive socio-technical lens on AI-driven cybersecurity, the work sheds light on the benefits and risks of artificial intelligence as well as the structural constraints of AI-enabled cyber defense mechanisms in general, and proposes research directions in this area, to ensure the future of AI-driven cybersecurity predictability, authenticity, and reliability.