Vol. 3 No. 2 (2023): Journal of AI-Assisted Scientific Discovery
Articles

Advanced Pod Security Standards in Amazon EKS with OPA

PDF
  • Babulal Shaik,
  • Sai Charith Daggupati
Babulal Shaik
Cloud Solutions Architect at Amazon Web Services, USA
Sai Charith Daggupati
Sr. IT BSA (Data systems) at CF Industries, USA
Cover

Published 24-09-2023

Keywords

  • Amazon EKS,
  • Pod Security Standards
Creative Commons License

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

How to Cite

[1]
Babulal Shaik and Sai Charith Daggupati, “Advanced Pod Security Standards in Amazon EKS with OPA ”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, pp. 660–682, Sep. 2023, Accessed: Dec. 30, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/257

Abstract

As adopting cloud-native technologies and containerized applications continues to grow, securing Kubernetes clusters has become a top priority for organizations. Amazon Elastic Kubernetes Service (EKS) offers a reliable solution for managing and scaling containerized applications, but with the flexibility of such platforms comes the responsibility of implementing stringent security measures. One of the key components to maintaining a secure environment is adhering to best practices for pod security. Advanced Pod Security Standards (APSS) is an evolving framework designed to protect containers and workloads in Kubernetes environments from vulnerabilities and security risks. Integrating APSS with Amazon EKS is essential for building a more secure, compliant, and reliable container orchestration system. This is where Open Policy Agent (OPA) comes into play. OPA is an open-source policy engine that enables the enforcement of fine-grained security policies across Kubernetes clusters. Organizations can enforce security rules at scale by using OPA to implement APSS, ensuring that only secure and compliant pods are deployed and run within the cluster. Integrating APSS with OPA allows for automated validation & continuous policy enforcement, reducing the chances of security breaches and improving the overall security posture of the environment. This approach helps ensure that the pods are compliant with internal security policies and aligned with industry standards and best practices. Through this integration, security administrators can define policies that limit the use of privileged containers, enforce image signature verification, restrict network capabilities, and impose other security measures critical to preventing the exploitation of vulnerabilities. Furthermore, using OPA allows for a more streamlined approach to compliance, eliminating the need for manual intervention and reducing the risk of human error in enforcing security policies. Organizations can also continuously monitor the status of their Kubernetes workloads & make adjustments as needed to ensure that their environments remain secure.

PDF

Downloads

Download data is not yet available.

References

  1. Creane, B., & Gupta, A. (2021). Kubernetes Security and Observability. " O'Reilly Media, Inc.".
  2. Huang, K., & Jumde, P. (2020). Learn Kubernetes Security: Securely orchestrate, scale, and manage your microservices in Kubernetes deployments. Packt Publishing Ltd.
  3. Creane, B., & Gupta, A. (2021). Kubernetes Security and Observability. " O'Reilly Media, Inc.".
  4. Domingus, J., & Arundel, J. (2022). Cloud Native DevOps with Kubernetes. " O'Reilly Media, Inc.".
  5. Brikman, Y. (2022). Terraform: Up and Running. " O'Reilly Media, Inc.".
  6. Mangels, F. (2020). Analyse der Sicherheit und der automatisierten Bereitstellung eines On-Premises-Clusters auf der Grundlage der Container-basierten Virtualisierung: Kubernetes im Wissenschaftsbetrieb (Doctoral dissertation, Hochschule Bremen).
  7. Ferreira, A. P., & Sinnott, R. (2019, December). A performance evaluation of containers running on managed kubernetes services. In 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) (pp. 199-208). IEEE.
  8. Baier, J., Sayfan, G., & White, J. (2019). The The Complete Kubernetes Guide: Become an expert in container management with the power of Kubernetes. Packt Publishing Ltd.
  9. Fornés-Leal, A., Lacalle, I., Palau, C. E., Szmeja, P., Ganzha, M., Paprzycki, M., ... & Blanquer, F. (2022). Assist-iot: A reference architecture for next generation internet of things. In New Trends in Intelligent Software Methodologies, Tools and Techniques (pp. 109-128). IOS Press.
  10. Saito, H., Lee, H. C. C., & Hsu, K. J. C. (2018). Kubernetes Cookbook: Practical solutions to container orchestration. Packt Publishing Ltd.
  11. Tomarchio, O., Calcaterra, D., & Modica, G. D. (2020). Cloud resource orchestration in the multi-cloud landscape: a systematic review of existing frameworks. Journal of Cloud Computing, 9(1), 49.
  12. Waxin Borén, F. (2021). Case study: Performance evaluation of Kind.
  13. Spinella, E. F. (2021). Event Streaming Open Network.
  14. Costa, A., & Jacob, A. (2018). OGC Earth Observation Exploitation Platform Hackathon 2018 Engineering Report.
  15. Can, U., & Alatas, B. (2019). A new direction in social network analysis: Online social network analysis problems and applications. Physica A: Statistical Mechanics and its Applications, 535, 122372.
  16. Boda, V. V. R., & Immaneni, J. (2022). Optimizing CI/CD in Healthcare: Tried and True Techniques. Innovative Computer Sciences Journal, 8(1).
  17. Immaneni, J. (2022). End-to-End MLOps in Financial Services: Resilient Machine Learning with Kubernetes. Journal of Computational Innovation, 2(1).
  18. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2022). The Shift Towards Distributed Data Architectures in Cloud Environments. Innovative Computer Sciences Journal, 8(1).
  19. Nookala, G. (2022). Improving Business Intelligence through Agile Data Modeling: A Case Study. Journal of Computational Innovation, 2(1).
  20. Komandla, V. Enhancing Product Development through Continuous Feedback Integration “Vineela Komandla”.
  21. Komandla, V. Enhancing Security and Growth: Evaluating Password Vault Solutions for Fintech Companies.
  22. Thumburu, S. K. R. (2022). Post-Migration Analysis: Ensuring EDI System Performance. Journal of Innovative Technologies, 5(1).
  23. Thumburu, S. K. R. (2022). Scalable EDI Solutions: Best Practices for Large Enterprises. Innovative Engineering Sciences Journal, 2(1).
  24. Gade, K. R. (2022). Data Catalogs: The Central Hub for Data Discovery and Governance. Innovative Computer Sciences Journal, 8(1).
  25. Gade, K. R. (2022). Data Lakehouses: Combining the Best of Data Lakes and Data Warehouses. Journal of Computational Innovation, 2(1).
  26. Katari, A., Ankam, M., & Shankar, R. Data Versioning and Time Travel In Delta Lake for Financial Services: Use Cases and Implementation.
  27. Katari, A. (2022). Performance Optimization in Delta Lake for Financial Data: Techniques and Best Practices. MZ Computing Journal, 3(2).
  28. Gade, K. R. (2021). Migrations: Cloud Migration Strategies, Data Migration Challenges, and Legacy System Modernization. Journal of Computing and Information Technology, 1(1).
  29. Thumburu, S. K. R. (2021). Performance Analysis of Data Exchange Protocols in Cloud Environments. MZ Computing Journal, 2(2).
  30. Boda, V. V. R., & Immaneni, J. (2019). Streamlining FinTech Operations: The Power of SysOps and Smart Automation. Innovative Computer Sciences Journal, 5(1).
  31. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2020). Data Virtualization as an Alternative to Traditional Data Warehousing: Use Cases and Challenges. Innovative Computer Sciences Journal, 6(1).
  32. Muneer Ahmed Salamkar. ETL Vs ELT: A Comprehensive Exploration of Both Methodologies, Including Real-World Applications and Trade-Offs. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Mar. 2019
  33. Muneer Ahmed Salamkar. Next-Generation Data Warehousing: Innovations in Cloud-Native Data Warehouses and the Rise of Serverless Architectures. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Apr. 2019
  34. Muneer Ahmed Salamkar. Real-Time Data Processing: A Deep Dive into Frameworks Like Apache Kafka and Apache Pulsar. Distributed Learning and Broad Applications in Scientific Research, vol. 5, July 2019
  35. Naresh Dulam, et al. “Apache Iceberg 1.0: The Future of Table Formats in Data Lakes”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, Feb. 2022, pp. 519-42
  36. Naresh Dulam, et al. “Kubernetes at the Edge: Enabling AI and Big Data Workloads in Remote Locations”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Oct. 2022, pp. 251-77
  37. Naresh Dulam, et al. “Data Mesh and Data Governance: Finding the Balance”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Dec. 2022, pp. 226-50
  38. Sarbaree Mishra. “Comparing Apache Iceberg and Databricks in Building Data Lakes and Mesh Architectures”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Nov. 2022, pp. 278-03
  39. Sarbaree Mishra. “Reducing Points of Failure - a Hybrid and Multi-Cloud Deployment Strategy With Snowflake”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, Jan. 2022, pp. 568-95
  40. Sarbaree Mishra, et al. “A Domain Driven Data Architecture for Data Governance Strategies in the Enterprise”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, Apr. 2022, pp. 543-67
  41. Babulal Shaik. Developing Predictive Autoscaling Algorithms for Variable Traffic Patterns . Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, July 2021, pp. 71-90
  42. Babulal Shaik, et al. Automating Zero-Downtime Deployments in Kubernetes on Amazon EKS . Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Oct. 2021, pp. 355-77

Most read articles by the same author(s)

Similar Articles

You may also start an advanced similarity search for this article.