Leveraging AI for Enhanced Security: A Technical Perspective
DOI:
https://doi.org/10.32628/CSEIT251112146Keywords:
Quantum-Enhanced Cybersecurity, AI-Driven Threat Detection, Automated Incident Response, Regulatory Compliance Automation, Pattern Recognition SystemsAbstract
This article explores the transformative impact of artificial intelligence on modern cybersecurity operations. The article examines how AI-driven systems have revolutionized threat detection, anomaly identification, and incident response capabilities while significantly improving operational efficiency in security operations centers. The article investigates advanced applications of quantum-enhanced pattern recognition, regulatory compliance automation, and automated incident response systems. Through detailed analysis of enterprise implementations, this article demonstrates how AI integration has enhanced security posture while reducing operational overhead. This article also addresses critical implementation considerations, including infrastructure requirements, data quality demands, and ethical implications of automated security decisions. This article provides insights into the practical challenges and strategic benefits of implementing AI-driven security solutions in enterprise environments.
Downloads
References
Irshaad Jada, Thembekile O. Mayayise, “The impact of artificial intelligence on organisational cyber security: An outcome of a systematic literature review,” June 2024, Available: https://www.sciencedirect.com/science/article/pii/S2543925123000372
Merve Ozkan, et al, “A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions,” January 2024, Available: https://www.researchgate.net/publication/377747343_A_Comprehensive_Survey_Evaluating_the_Efficiency_of_Artificial_Intelligence_and_Machine_Learning_Techniques_on_Cyber_Security_Solutions
Maloy Jyoti Goswami, “AI-Based Anomaly Detection for Real-Time Cybersecurity," February 2024, Available: https://www.researchgate.net/publication/381044167_AI-Based_Anomaly_Detection_for_Real-Time_Cybersecurity
Alqasim Shamshari, et al, “Machine Learning Approaches for Anomaly Detection in Network Security,” March 2024, Available: https://www.researchgate.net/publication/378966802_Machine_Learning_Approaches_for_Anomaly_Detection_in_Network_Security
Emma Nuyts, Mathias Bonduel, Ruben Verstraeten, et al, “Comparative analysis of approaches for automated compliance checking of construction data,” April 2024, Available: https://www.sciencedirect.com/science/article/pii/S1474034624000910
Dr.Dankan V Gowda, et al, “Quantum Cryptography and Machine Learning: Enhancing Security in AI Systems,” October 2024, Available: https://www.researchgate.net/publication/385192227_Quantum_Cryptography_and_Machine_Learning_Enhancing_Security_in_AI_Systems
Adesokan Ayodeji, “Artificial Intelligence in Enhancing Regulatory Compliance and Risk Management,” June 2024, Available: https://www.researchgate.net/publication/381045225_Artificial_Intelligence_in_Enhancing_Regulatory_Compliance_and_Risk_Management
Kaledio Potter, Lucas Doris, “AI-POWERED THREAT DETECTION AND INCIDENT RESPONSE SYSTEMS,” November 2024, Available: https://www.researchgate.net/publication/385445343_AI-POWERED_THREAT_DETECTION_AND_INCIDENT_RESPONSE_SYSTEMS
Mohammad I. Merhi, “An evaluation of the critical success factors impacting artificial intelligence implementation,” April 2023, Available: https://www.sciencedirect.com/science/article/abs/pii/S0268401222000792
Sundeep Mamidi, “Integrating AI into Legacy Security Systems,” August 2024, Available : https://www.researchgate.net/publication/383915519_Integrating_AI_into_Legacy_Security_Systems
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.