Leveraging AI for Enhanced Security: A Technical Perspective

Authors

  • Deepak Bhaskaran Cisco Systems Inc, USA Author

DOI:

https://doi.org/10.32628/CSEIT251112146

Keywords:

Quantum-Enhanced Cybersecurity, AI-Driven Threat Detection, Automated Incident Response, Regulatory Compliance Automation, Pattern Recognition Systems

Abstract

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

Download data is not yet available.

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

03-02-2025

Issue

Section

Research Articles

How to Cite

Leveraging AI for Enhanced Security: A Technical Perspective. (2025). International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 1448-1455. https://doi.org/10.32628/CSEIT251112146