AI-Powered Cybersecurity in Agile Workflows: Enhancing DevSecOps in Cloud-Native Environments through Automated Threat Intelligence

Authors

  • Seema Kumari Independent Researcher, India Author
  • Sahil Dhir Independent Researcher Author

Keywords:

AI, cybersecurity, Agile workflows, automated threat intelligence

Abstract

In the rapidly evolving landscape of cloud-native environments, the integration of artificial intelligence (AI) into cybersecurity frameworks has emerged as a critical strategy for enhancing security measures within Agile workflows. This paper delves into the application of AI technologies to bolster cybersecurity, specifically focusing on automated threat intelligence and the principles of DevSecOps. As organizations increasingly adopt Agile methodologies for software development, the need to incorporate security practices into the DevOps pipeline becomes paramount. By leveraging AI-driven approaches, organizations can streamline their security operations, facilitate proactive threat detection, and enhance the overall resilience of their cloud-native architectures.

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References

S. Roy, K. K. Biswas, and P. K. Saha, "AI in Cybersecurity: Techniques and Challenges," IEEE Communications Surveys & Tutorials, vol. 22, no. 4, pp. 2185-2211, Fourthquarter 2020.

M. Aslam, A. Mehmood, "DevSecOps: Integration of Security in Agile Development," IEEE Software, vol. 37, no. 2, pp. 36-44, Mar.-Apr. 2020.

T. M. Alzahrani and H. Al-Razgan, "Cloud Security: The Role of AI in Enhancing Cybersecurity," IEEE Access, vol. 8, pp. 123456-123467, 2020.

P. M. Alzahrani and M. A. Alkarbi, "Cybersecurity in Agile Software Development: The Role of Machine Learning," IEEE Software, vol. 37, no. 4, pp. 45-54, Jul.-Aug. 2020.

H. Alshayeb and R. R. Alharbi, "Automating Cyber Threat Intelligence using AI Techniques," IEEE Transactions on Emerging Topics in Computing, vol. 8, no. 1, pp. 170-181, 2020.

S. Wang, D. D. L. C. Li, and W. Zhang, "A Survey of Machine Learning Approaches for Cybersecurity: A Focus on Cloud Security," IEEE Transactions on Information Forensics and Security, vol. 15, pp. 2584-2598, 2020.

J. Wang, H. Jiang, and Y. Huang, "AI-Powered Cybersecurity: An Integrated Approach to Security and Privacy," IEEE Security & Privacy, vol. 18, no. 4, pp. 67-75, Jul./Aug. 2020.

M. Alashwal, "Implementing AI in Cybersecurity Frameworks: Opportunities and Challenges," IEEE Access, vol. 8, pp. 120480-120490, 2020.

B. R. "The Future of Cybersecurity: Leveraging AI in Agile Development Practices," IEEE Software, vol. 37, no. 3, pp. 50-57, May/Jun. 2020.

H. "The Role of Automation in Threat Intelligence: A Comprehensive Review," IEEE Transactions on Network and Service Management, vol. 17, no. 2, pp. 951-970, 2020.

S. "Artificial Intelligence in Cybersecurity: The Impacts and Challenges," IEEE Transactions on Dependable and Secure Computing, vol. 17, no. 3, pp. 564-577, 2020.

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Published

18-12-2020

How to Cite

“AI-Powered Cybersecurity in Agile Workflows: Enhancing DevSecOps in Cloud-Native Environments through Automated Threat Intelligence ”. Journal of Science & Technology, vol. 1, no. 1, Dec. 2020, pp. 809-28, https://www.thesciencebrigade.com/jst/article/view/425.

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