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

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.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-11-20
DOI
https://doi.org/10.5281/zenodo.14191399
Primary Topic
Information and Cyber Security
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Zenodo (CERN European Organization for Nuclear Research)
Information and Cyber Security
article

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

article en

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.

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Openalex Percentile: Top 38%
Information and Cyber Security
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