Enhancing Cybersecurity Resilience Through AI-Driven Threat Detection and Automated Incident Response in Modern Networks

Jhon Aravid

Authors

Jhon Aravid India Author

Keywords:

Cybersecurity, Artificial Intelligence, Threat Detection, Automated Incident Response, Machine Learning, Deep Learning, Network Security

Abstract

The rapid evolution of cyber threats has necessitated advanced security solutions capable of real-time threat detection and automated incident response. Traditional cybersecurity methods struggle to keep pace with sophisticated attacks, necessitating AI-driven approaches for proactive defense. This paper explores the integration of machine learning (ML), deep learning (DL), and automation in modern cybersecurity frameworks to detect, prevent, and mitigate cyber threats. AI-based threat intelligence enhances network monitoring by analyzing anomalies and predicting potential risks before they escalate. Automated incident response mechanisms enable faster remediation, reducing downtime and minimizing the impact of attacks. A comparative analysis highlights the superior performance of AI-driven models over traditional security measures. The findings demonstrate that AI-powered cybersecurity solutions significantly improve threat detection accuracy, response speed, and overall system resilience.

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