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article · 2025

AI-Driven Threat Intelligence for Proactive Cybersecurity in Enterprise Networks

Danish Ather, Amanpreet Singh, Manik Rakhra, Rajneesh Kler, Gaurav Aggarwal, Kapil Jairath

Cited by 8Abstract and details only

Abstract

Today, new and advanced attacks arise and for this reason, effective mechanisms for the protection of enterprise networks must be developed. AI based threat intelligence systems provide the feature of continuous monitoring, big data analytics and prediction that helps to improve proactive security. This paper focus on the application of AI in threat identification, abnormality identification and response Automation with an aim of illustrating their roles in the prevention of emerging new threats. Thereby, the study identifies current trends in enterprise cybersecurity and provide case studies to show how AI-based cybersecurity frameworks can help organisations adopt a strong and dynamic security stance against sophisticated cyber threats.

Cite this paper

APA 7

Ather, D., Singh, A., Rakhra, M., Kler, R., Aggarwal, G., & Jairath, K. (2025). AI-Driven Threat Intelligence for Proactive Cybersecurity in Enterprise Networks. https://doi.org/10.1109/netcrypt65877.2025.11102400

MLA 9

Ather, Danish, et al. “AI-Driven Threat Intelligence for Proactive Cybersecurity in Enterprise Networks.” 2025, pp. 1720–25, https://doi.org/10.1109/netcrypt65877.2025.11102400.

Chicago

Ather, Danish, Amanpreet Singh, Manik Rakhra, Rajneesh Kler, Gaurav Aggarwal, and Kapil Jairath. 2025. “AI-Driven Threat Intelligence for Proactive Cybersecurity in Enterprise Networks.” https://doi.org/10.1109/netcrypt65877.2025.11102400.

Harvard

Ather, D. et al. (2025) ‘AI-Driven Threat Intelligence for Proactive Cybersecurity in Enterprise Networks’. Available at: https://doi.org/10.1109/netcrypt65877.2025.11102400.

BibTeX
@article{ather2025driven,
  title = {{AI-Driven Threat Intelligence for Proactive Cybersecurity in Enterprise Networks}},
  author = {Ather, Danish and Singh, Amanpreet and Rakhra, Manik and Kler, Rajneesh and Aggarwal, Gaurav and Jairath, Kapil},
  pages = {1720--1725},
  year = {2025},
  doi = {10.1109/netcrypt65877.2025.11102400}
}

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