Understanding AI-Driven Threat Detection and Response Systems: A Technical Deep Dive
DOI:
https://doi.org/10.32628/CSEIT251112324Keywords:
Artificial Intelligence Security, Behavioral Analytics, Automated Threat Response, Machine Learning Detection, Quantum CybersecurityAbstract
This article presents a comprehensive analysis of artificial intelligence-driven threat detection and response systems in modern cybersecurity environments. The article examines the evolution of security infrastructure through the integration of advanced machine learning algorithms and automated response mechanisms. The article investigates the effectiveness of multi-tiered machine learning architectures in threat detection, behavioral analysis frameworks for user and device monitoring, and automated response capabilities. The article demonstrates significant improvements in detection accuracy, response times, and false positive reduction through AI optimization. The article indicates that the integration of quantum computing and advanced AI capabilities presents promising developments for future security systems, while maintaining operational efficiency and scalability across enterprise deployments.
Downloads
References
Ramanpreet Kaur, et al, “Artificial intelligence for cybersecurity: Literature review and future research directions,” 2023, Available: https://www.sciencedirect.com/science/article/pii/S1566253523001136
Venkata Tadi, “Quantitative Analysis of AI-Driven Security Measures: Evaluating Effectiveness, Cost-Efficiency, and User Satisfaction Across Diverse,” 2024, Available : https://jsaer.com/download/vol-11-iss-4-2024/JSAER2024-11-4-328-343.pdf
Rasheed Yousef, et al, “Measuring the Effectiveness of User and Entity Behavior Analytics for the Prevention of Insider Threats,” October 2021, Available : https://www.researchgate.net/publication/355424926_Measuring_the_Effectiveness_of_User_and_Entity_Behavior_Analytics_for_the_Prevention_of_Insider_Threats
Louisa Saunier, “Next-Generation Security,” 2020, Available: https://www.isaca.org/resources/isaca-journal/issues/2020/volume-5/next-generation-security
Aya H. Salem, et al, “Advancing cybersecurity: a comprehensive review of AI-driven detection techniques,” 04 August 2024, Available: https://journalofbigdata.springeropen.com/articles/10.1186/s40537-024-00957-y
Uchenna Joseph Umoga, et al, “Exploring the potential of AI-driven optimization in enhancing network performance and efficiency,” February 2024, Available : https://www.researchgate.net/publication/378666643_Exploring_the_potential_of_AI-driven_optimization_in_enhancing_network_performance_and_efficiency
Zaheer Abbas, et al, “Enterprise Integration in Modern Cloud Ecosystems: Patterns, Strategies, and Tools,” December 2017, Available : https://www.researchgate.net/publication/386554693_Enterprise_Integration_in_Modern_Cloud_Ecosystems_Patterns_Strategies_and_Tools
Abdullah Hill Hussain, et al, “Enhancing cyber security using quantum computing and Artificial Intelligence: A review,” 2021, Available: https://wjarr.com/sites/default/files/WJARR-2021-0196.pdf
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology

This work is licensed under a Creative Commons Attribution 4.0 International License.