Enhancing Cybersecurity in Cloud Environments Using AI-Driven Threat Detection and Response

Harshvardhan Chunawala; Pratikkumar Chunawala

doi:10.59367/2420ra43

Authors

Harshvardhan Chunawala
Pratikkumar Chunawala

DOI:

https://doi.org/10.59367/2420ra43

Keywords:

AI-driven threat detectio, cloud cybersecurity, automated incident response, machine learning, cloud security

Abstract

As cloud computing becomes increasingly integral to modern infrastructure, the importance of robust cybersecurity measures within cloud environments cannot be overstated. Traditional security approaches often fall short in addressing the dynamic and complex nature of cloud-based threats. This paper explores the application of artificial intelligence (AI) to enhance cybersecurity in cloud environments, with a focus on AI-driven threat detection and response systems. By leveraging machine learning algorithms and deep learning models, AI can analyze vast amounts of data in real-time, identifying anomalies and potential threats with greater accuracy and speed than conventional methods. This research presents a comprehensive framework that integrates AI-driven solutions for proactive threat detection, automated incident response, and continuous security monitoring. The framework is designed to adapt to evolving threats, offering a scalable and efficient defense mechanism against sophisticated cyber-attacks. This paper includes case studies and experimental evaluations that demonstrate the effectiveness of AI-based approaches in reducing false positives, improving detection rates, and accelerating response times. The findings underscore AI’s critical role in advancing cloud security and protecting sensitive data in an increasingly digital world. The results indicate that AI-driven cybersecurity systems significantly enhance the security posture of cloud environments, making them more resilient against emerging threats. This study concludes with a discussion on the challenges and future directions for AI in cybersecurity, emphasizing the need for ongoing research to address issues such as model interpretability, data privacy, and the integration of AI with existing security infrastructures.

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