AI-DRIVEN THREAT INTELLIGENCE FOR REAL-TIME NETWORK SECURITY OPTIMIZATION

Authors

  • Srikanth Bellamkonda Barclays Services Corporation, USA. Author

Keywords:

AI-Driven Security, Threat Intelligence, Network Security Optimization, Machine Learning Models, Real-Time Threat Detection

Abstract

Artificial Intelligence (AI) revolutionizes network security through advanced threat intelligence and automated response mechanisms. This article investigates the integration of AI-driven frameworks in cybersecurity, examining their impact on threat detection, incident response, and overall security posture. The article demonstrates significant improvements in security operations through a comprehensive analysis of enterprise implementations, including reduced false positives, enhanced threat detection accuracy, and streamlined incident response processes. The article presents a multi-layered framework incorporating data collection, analysis, decision-making, and response components, validated through multiple case studies across financial, healthcare, and e-commerce sectors. The findings highlight how AI-driven security solutions effectively address the limitations of traditional security approaches while introducing new considerations for implementation and optimization.

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Published

2024-11-20

How to Cite

Srikanth Bellamkonda. (2024). AI-DRIVEN THREAT INTELLIGENCE FOR REAL-TIME NETWORK SECURITY OPTIMIZATION. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY (IJCET), 15(6), 522-534. https://mylib.in/index.php/IJCET/article/view/IJCET_15_06_044