Vol. 4 No. 2 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

Developing AI-Augmented Security Models for Amazon EKS Workloads

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  • Babulal Shaik
Babulal Shaik
Cloud Solutions Architect at Amazon Web Services, USA

Published 30-07-2024

Keywords

  • Amazon EKS,
  • AI security models
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Babulal Shaik, “Developing AI-Augmented Security Models for Amazon EKS Workloads ”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 2, pp. 105–124, Jul. 2024, Accessed: Jan. 30, 2025. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/224

Abstract

As organizations embrace cloud-native technologies, securing their workloads has become paramount. Amazon Elastic Kubernetes Service (EKS) is a widely adopted solution for managing containerized applications, but it brings a unique set of security challenges. To address these challenges, integrating AI-driven security models offers a promising approach. This paper explores the potential of AI technologies such as machine learning, anomaly detection, & predictive analytics to enhance the security of EKS workloads. It starts by identifying the key security risks organizations face using Amazon EKS, including vulnerabilities in container orchestration, unauthorized access, and the complexities of managing dynamic environments. The paper then examines how AI can be applied to these challenges, offering solutions that respond to threats in real-time and predict and mitigate potential risks before they manifest. AI can analyze vast amounts of data from EKS environments through machine learning models, identifying patterns that may signal malicious activity or system vulnerabilities. Anomaly detection techniques can monitor container behaviour, flagging deviations from normal operations that could indicate a security breach. On the other hand, predictive analytics can help organizations anticipate potential threats, providing proactive measures for risk mitigation. By incorporating these AI-driven approaches, organizations can enhance their ability to protect EKS workloads from emerging threats and optimize their security strategies. Integrating AI technologies can significantly reduce incident response time, automate threat detection, & provide deeper insights into system behaviour. This paper highlights how AI can complement traditional EKS security practices, offering a more robust, adaptive, and predictive security framework. Organizations looking to secure their EKS workloads can benefit from the guidance, leveraging AI to improve their defence mechanisms and stay ahead of evolving security challenges in the cloud-native space.

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