AI-POWERED CYBERSECURITY IN DEVOPS: LEVERAGING DATA SCIENCE TO PREDICT AND MITIGATE SECURITY THREATS
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Swathi Suddala, AI-Powered Cybersecurity In Devops, Leveraging Data Science To Predict, Mitigate Security ThreatsAbstract
As organizations increasingly adopt DevOps practices to deliver software faster and more efficiently, integrating robust cybersecurity measures becomes more critical. Traditional security measures can slow down the continuous integration and delivery (CI/CD) pipeline, exposing vulnerabilities to attackers. Artificial intelligence (AI) and data science provide a solution by enabling rapid, predictive, and automated cybersecurity practices. This paper explores how AI-powered cybersecurity tools can be integrated into DevOps environments, leveraging data science techniques to predict and mitigate security threats in real time. Through predictive analytics, AI can help identify potential vulnerabilities and proactively address them, significantly enhancing security while maintaining the agility of DevOps workflows.
References
Mavroeidis, V., & Bromander, S. (2017). Cyber Threat Intelligence Model: An Evaluation of Taxonomies, Sharing Standards, and Ontologies within Cyber Threat Intelligence. Procedia Computer Science, 1-10.
Korczynski, M., & Duda, A. (2019). Mitigating Security Threats in DevOps Environments Using AI. Journal of
Information Security, 24-30.
Shiloach, Y. (2020). The Role of AI in Modern Cybersecurity. IEEE Security & Privacy, 35-40.
Sarker IH, Kayes ASM, Badsha S, Alqahtani H, Watters P, Ng A. Cybersecurity data science: an overview from machine learning perspective. J Big Data. 2020;7(1):1–29.
https://link.springer.com/article/10.1186/s40537-020-00318-5
Ibm security report. https://www.ibm.com/security/data-breach. Accessed 20 Oct 2019.
Fischer EA. Cybersecurity issues and challenges: in brief. 2014.
Mohammadi S, Mirvaziri H, Ghazizadeh-Ahsaee M, Karimipour H. Cyber intrusion detection by combined feature selection algorithm. J Inf Secur Appl. 2019;44:80–8.
https://www.mdpi.com/1999-4893/10/2/39
Foroughi F, Luksch P. Data science methodology for cybersecurity projects. arXiv preprint arXiv:1803.04219. 2018.
Saxe J, Sanders H. Malware data science: attack detection and attribution. 2018.
Rainie L, Anderson J, Connolly J. Cyber-attacks likely to increase. Digit Life. 2014;2025.
Kim, T., & Shon, T. (2020). Cybersecurity in DevOps: Security automation in cloud environments using artificial intelligence. Journal of Cloud Computing, 9(1), 1-16.
He, Y., & Xu, C. (2017). Deep learning-based anomaly detection in cybersecurity: A survey and taxonomy. Computers & Security, 73, 1-21.
Wang, Y., & Guan, Y. (2018). AI-driven vulnerability management in continuous delivery pipelines. Proceedings of the ACM Workshop on Continuous Integration and Delivery.
Ucci, D., Aniello, L., & Baldoni, R. (2019). Survey on the usage of artificial intelligence techniques in malware detection. Journal of Big Data, 6(1), 1-20.
Bhattacharya, P., & Hossain, M. A. (2019). Predictive analytics in cybersecurity: Leveraging machine learning for detecting potential threats. Cybersecurity Journal, 6(2), 120-135.
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Copyright (c) 2022 Swathi Suddala (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
