(untitled) (International Journal of Latest Technology in Engineering Management & Applied Science, 2026)
Recent advances in digital infrastructures, cloud services, blockchain platforms, Internet of Things (IoT), and artificial intelligence (AI) have significantly increased the complexity of cybersecurity threats while intensifying concerns related to data privacy and system trust. In response, contemporary research has focused on integrating intelligent data-driven techniques with secure and privacy-aware mechanisms to counter sophisticated cyberattacks. This literature review systematically analyzes and synthesizes selected peer-reviewed studies with an emphasis on AI-driven intrusion detection systems and blockchain-enabled security architectures. The reviewed works are examined in terms of their underlying methodologies, algorithms, tools, strengths, and limitations. A comparative analysis identifies key trends, persistent challenges, and research gaps, particularly in explainable AI, system scalability, and secure analytics. The findings highlight the need for unified and deployable cybersecurity frameworks that balance detection accuracy, transparency, and operational efficiency. This review provides a structured foundation to support future research on intelligent and trustworthy cybersecurity systems.