Techniques for Encrypting Data in Amazon EKS for Delicate Apps
Keywords:
Amazon EKS, data encryption, sensitive applications, KubernetesAbstract
Protecting sensitive apps requires more data security, particularly when companies move to cloud platforms like Amazon Elastic Kubernetes Services to handle workloads at scale. Strong encryption techniques are very necessary for any data security in Elastic Kubernetes Service collections in order to safeguards any private data while it is in transit or at rest. The encryption methods for protecting data in Elastic Kubernetes Service are more highlighted in this paper. Features that protect sensitive data from unwanted access includes encryption for Amazon EBS volumes, S3 buckets & other persistent storages for data at rest. Kubernetes reduces vulnerabilities like man-in-the-middle attacks by supporting TLS to encrypts communication between services for data in transit. Secure storages & frequent key rotations are two most crucial best practices for encryption key managements. By automating encryption activities & streamlining key managements, AWS Key Management Services (KMS) improve security in Kubernetes deployments. In addition to encryption, it's very critical to monitor & audit any sensitive data access. The integrity & confidentiality of data are protected by methods & tools for identifying weaknesses & handling breaches. Organizations may also protect critical apps on Elastic Kubernetes Service, attain compliance & lower the risk of cloud data breaches by adhering to these best practices & using the appropriate technologies.
References
1. Sanka, S., Hota, C., & Rajarajan, M. (2010, December). Secure data access in cloud computing. In 2010 IEEE 4th International Conference on Internet Multimedia Services Architecture and Application (pp. 1-6). IEEE.
2. Hota, C., Sanka, S., Rajarajan, M., & Nair, S. K. (2011). Capability-based cryptographic data access control in cloud computing. International Journal of Advanced Networking and Applications, 3(3), 1152-1161.
3. Jurvanen, K. J. (2021). Using AWS Secrets Manager with Kubernetes.
4. Koletka, R., & Hutchison, A. (2011, August). An architecture for secure searchable cloud storage. In 2011 Information Security for South Africa (pp. 1-7). IEEE.
5. Kaaniche, N., & Laurent, M. (2017). Data security and privacy preservation in cloud storage environments based on cryptographic mechanisms. Computer Communications, 111, 120-141.
6. Krishna, S. R., & Rani, B. P. (2013). Security Enhancement through Fine Grained Access Control in Cloud Computing. International Journal of Advanced Research in Computer Science, 4(11).
7. Tysowski, P. K., & Hasan, M. A. (2013, January). Cloud-hosted key sharing towards secure and scalable mobile applications in clouds. In 2013 International Conference on Computing, Networking and Communications (ICNC) (pp. 449-455). IEEE.
8. Routavaara, I. (2020). Security monitoring in AWS public cloud.
9. Gómez Escobar, J. A. (2019). Design of a reference architecture for an IoT sensor network.
10. Rocha, F. E. L. (2010). Privacy in cloud computing (Doctoral dissertation).
11. Carnell, J., & Sánchez, I. H. (2021). Spring microservices in action. Simon and Schuster.
12. Atwal, H., & Atwal, H. (2020). Dataops technology. Practical DataOps: Delivering Agile Data Science at Scale, 215-247.
13. Zhang, Y., Li, M., Wilder, B., Yu, M., Bai, K., & Liu, P. (2011). NeuCloud: enabling privacy-preserving monitoring in cloud computing. View at.
14. Medel Gracia, V., & ARRONATEGUI ARRIBALZAGA, U. N. A. I. (2018). Application Driven MOdels for Resource Management in Cloud Environments (Doctoral dissertation, Universidad de Zaragoza, Prensas de la Universidad).
15. Bae, E. (2014). Nation-State Cyber Surveillance Options: The role of suppliers (Master's thesis).
16. Boda, V. V. R., & Immaneni, J. (2021). Healthcare in the Fast Lane: How Kubernetes and Microservices Are Making It Happen. Innovative Computer Sciences Journal, 7(1).
17. Immaneni, J. (2021). Using Swarm Intelligence and Graph Databases for Real-Time Fraud Detection. Journal of Computational Innovation, 1(1).
18. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2021). Unified Data Architectures: Blending Data Lake, Data Warehouse, and Data Mart Architectures. MZ Computing Journal, 2(2).
19. Nookala, G. (2021). Automated Data Warehouse Optimization Using Machine Learning Algorithms. Journal of Computational Innovation, 1(1).
20. Komandla, V. Strategic Feature Prioritization: Maximizing Value through User-Centric Roadmaps.
21. Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
22. Thumburu, S. K. R. (2021). A Framework for EDI Data Governance in Supply Chain Organizations. Innovative Computer Sciences Journal, 7(1).
23. Thumburu, S. K. R. (2021). EDI Migration and Legacy System Modernization: A Roadmap. Innovative Engineering Sciences Journal, 1(1).
24. Gade, K. R. (2021). Cost Optimization Strategies for Cloud Migrations. MZ Computing Journal, 2(2).
25. Gade, K. R. (2021). Cloud Migration: Challenges and Best Practices for Migrating Legacy Systems to the Cloud. Innovative Engineering Sciences Journal, 1(1).
26. Katari, A. Conflict Resolution Strategies in Financial Data Replication Systems.
27. Katari, A., & Rallabhandi, R. S. DELTA LAKE IN FINTECH: ENHANCING DATA LAKE RELIABILITY WITH ACID TRANSACTIONS.
28. Boda, V. V. R., & Immaneni, J. (2019). Streamlining FinTech Operations: The Power of SysOps and Smart Automation. Innovative Computer Sciences Journal, 5(1).
29. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2020). Automating ETL Processes in Modern Cloud Data Warehouses Using AI. MZ Computing Journal, 1(2).
30. Thumburu, S. K. R. (2020). Interfacing Legacy Systems with Modern EDI Solutions: Strategies and Techniques. MZ Computing Journal, 1(1).
31. Muneer Ahmed Salamkar, et al. The Big Data Ecosystem: An Overview of Critical Technologies Like Hadoop, Spark, and Their Roles in Data Processing Landscapes. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Sept. 2021, pp. 355-77
32. Muneer Ahmed Salamkar. Scalable Data Architectures: Key Principles for Building Systems That Efficiently Manage Growing Data Volumes and Complexity. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, Jan. 2021, pp. 251-70
33. Muneer Ahmed Salamkar, and Karthik Allam. “Data Lakes Vs. Data Warehouses: Comparative Analysis on When to Use Each, With Case Studies Illustrating Successful Implementations”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
34. Naresh Dulam, et al. “Data Mesh in Action: Case Studies from Leading Enterprises”. Journal of Artificial Intelligence Research and Applications, vol. 1, no. 2, Dec. 2021, pp. 488-09
35. Naresh Dulam, et al. “Real-Time Analytics on Snowflake: Unleashing the Power of Data Streams”. Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, July 2021, pp. 91-114
36. Naresh Dulam, and Karthik Allam. “Snowflake Innovations: Expanding Beyond Data Warehousing ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Apr. 2019
37. Sarbaree Mishra. “Improving the Data Warehousing Toolkit through Low-Code No-Code”. Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, Oct. 2021, pp. 115-137
38. Sarbaree Mishra, and Jeevan Manda. “Incorporating Real-Time Data Pipelines Using Snowflake and Dbt”. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, Mar. 2021, pp. 205-2
39. Sarbaree Mishra, et al. Training Models for the Enterprise - A Privacy Preserving Approach. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Mar. 2019
40. Babulal Shaik. Network Isolation Techniques in Multi-Tenant EKS Clusters. Distributed Learning and Broad Applications in Scientific Research, vol. 6, July 2020
41. Babulal Shaik. Automating Compliance in Amazon EKS Clusters With Custom Policies . Journal of Artificial Intelligence Research and Applications, vol. 1, no. 1, Jan. 2021, pp. 587-610
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
