Dynamic Security Compliance Checks in Amazon EKS for Regulated Industries

Authors

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

Keywords:

Amazon EKS, regulated industries

Abstract

In regulated industries such as healthcare and finance, stringent security and compliance measures are critical to protect sensitive data and meet industry-specific regulations. As more organizations migrate to cloud-native environments, Amazon Elastic Kubernetes Service (EKS) has become a popular solution for managing containerized applications. However, ensuring compliance in such dynamic environments presents unique challenges, particularly in industries with rigorous regulatory standards like HIPAA and PCI-DSS. This paper proposes a framework to enforce dynamic security compliance checks within Amazon EKS, explicitly designed for the evolving needs of healthcare and financial services. The framework leverages AWS's native tools, including AWS Config, AWS CloudTrail, and AWS Security Hub, to automate compliance checks and continuously monitor security posture in real-time. By integrating industry best practices and utilizing cloud-native security tools, the framework ensures that security and compliance requirements are met seamlessly without sacrificing the cloud infrastructure's agility and scalability. The approach emphasizes the importance of automation in compliance management, enabling organizations to continuously validate their security posture and respond to potential threats with minimal manual intervention. Additionally, the framework supports real-time auditing and reporting, making it easier for organizations to demonstrate compliance during inspections and audits. By embedding security and compliance checks directly into the development and deployment pipeline, the solution minimizes non-compliance risk and ensures that regulatory requirements are continuously enforced. This paper highlights the critical role of continuous monitoring and automated security tools in overcoming compliance challenges in regulated industries. The proposed framework offers a scalable, effective solution for organizations looking to maintain regulatory compliance while ensuring the flexibility and performance that cloud-native technologies provide. It offers a practical path forward for achieving secure, compliant operations in complex, fast-paced cloud environments like Amazon EKS.

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References

Kaaniche, N., & Laurent, M. (2017). Data security and privacy preservation in cloud storage environments based on cryptographic mechanisms. Computer Communications, 111, 120-141.

Tran, K. (2011). Building virtual lab with amazon cloud services (Doctoral dissertation, Minnesota State University, Mankato).

Sayfan, G. (2018). Mastering Kubernetes: Master the art of container management by using the power of Kubernetes. Packt Publishing Ltd.

Danidou, I. (2017). Trusted Computing or trust in computing? Legislating for trust networks.

Umachandran, K. (2007). Study of timber market of Malaysia and its impact on the economy and employment. Education, 2010.

Naruchitparames, J. (2011). Enhancing the privacy of data communications within information-sensitive systems (Doctoral dissertation).

Díaz-Sánchez, D., Sánchez-Guerrero, R., Arias, P., Almenarez, F., & Marín, A. (2016). A distributed transcoding and content protection system: Enabling pay per quality using the cloud. Telecommunication Systems, 61, 59-76.

Aw Ideler, H. (2012). Cryptography as a service in a cloud computing environment. EINDHOVEN UNIVERSITY OF TECHNOLOGY, Department of Mathematics and Computing Science.

Paladi, N. (2017). Trust but verify: trust establishment mechanisms in infrastructure clouds.

Dhotre, P. S. (2017). Systematic Analysis and Visualization of Privacy Policies of Online Services.

Willems, E. K. S. (2004). Environmental Sociology and the Risk Debate: Insights from the Brazilian and British Biotechnology Controversy.

Birk, F. (2018). Design and Implementation of a Scalable Crowdsensing Platform for Geospatial Data (Doctoral dissertation, Ulm University).

Bischoff, M. (2018). Design and implementation of a framework for validating kubernetes policies through automatic test generation (Doctoral dissertation, Ph. D. dissertation, Hochschule der Medien Stuttgart).

Gracia, V. M. (2018). Application driven models for resource management in cloud environments (Doctoral dissertation, Universidad de Zaragoza).

Mansuroglu, D. (2008). Using RFID In Augmented Campus Environments.

Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.

Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).

Gade, K. R. (2017). Integrations: ETL vs. ELT: Comparative analysis and best practices. Innovative Computer Sciences Journal, 3(1).

Naresh Dulam. NoSQL Vs SQL: Which Database Type Is Right for Big Data?. Distributed Learning and Broad Applications in Scientific Research, vol. 1, May 2015, pp. 115-3

Naresh Dulam. Machine Learning on Kubernetes: Scaling AI Workloads . Distributed Learning and Broad Applications in Scientific Research, vol. 2, Sept. 2016, pp. 50-70

Naresh Dulam, et al. Apache Arrow: Optimizing Data Interchange in Big Data Systems. Distributed Learning and Broad Applications in Scientific Research, vol. 3, Oct. 2017, pp. 93-114

Naresh Dulam, et al. Apache Iceberg: A New Table Format for Managing Data Lakes . Distributed Learning and Broad Applications in Scientific Research, vol. 4, Sept. 2018

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Published

02-05-2019

How to Cite

[1]
Babulal Shaik, “Dynamic Security Compliance Checks in Amazon EKS for Regulated Industries”, Distrib Learn Broad Appl Sci Res, vol. 5, pp. 1369–1385, May 2019, Accessed: Dec. 29, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/257

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