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DOI: https://doi.org/10.63345/ijrmeet.org.v10.i11.9
Dr. Shreya Kulkarni
Independent Researcher
Maharashtra, India
Abstract
Data privacy compliance in the cloud is one of the most pressing concerns for modern enterprises, especially as businesses move towards leveraging cloud service providers like AWS (Amazon Web Services) for scalability and flexibility. This paper examines the strategies and challenges related to ensuring data privacy compliance within the AWS cloud ecosystem. It explores the legal and regulatory frameworks that organizations must navigate, such as GDPR, CCPA, and HIPAA, and how AWS provides tools to assist in compliance. Additionally, the paper investigates the technical and organizational strategies enterprises need to implement to achieve and maintain compliance, such as encryption, access control, and data classification. Despite the robust offerings from AWS, numerous challenges remain in managing data privacy, including inconsistent data handling practices, lack of awareness of shared responsibility models, and evolving privacy laws. This study presents a detailed analysis of these issues, providing a comprehensive understanding of how organizations can align their data privacy practices with regulatory requirements in the cloud. Finally, the paper discusses future trends and developments in AWS that could further streamline data privacy compliance processes for enterprises.
Keywords
AWS, Data Privacy, Compliance, GDPR, CCPA, HIPAA, Cloud Security, Cloud Regulations.
References
- Soveizi, N., Turkmen, F., & Karastoyanova, D. (2022). Security and privacy concerns in cloud-based scientific and business workflows: A systematic review. arXiv preprint arXiv:2210.02161. org
- Tiwari, S. (2022). Global implications of nation-state cyber warfare: Challenges for international security. International Journal of Research in Modern Engineering and Emerging Technology (IJRMEET), 10(3), 42. https://doi.org/10.63345/ijrmeet.org.v10.i3.6s
- Klymenko, O., Meisenbacher, S., & Matthes, F. (2022). Identifying practical challenges in the implementation of technical measures for data privacy compliance. In Proceedings of the 11th International Conference on Business Process Management Workshops (BPM 2022 Workshops). CEUR Workshop Proceedings. org
- Shastri, S., Wasserman, M., & Chidambaram, V. (2019). GDPR anti-patterns: How design and operation of modern cloud-scale systems conflict with GDPR. arXiv preprint arXiv:1911.00498. org
- Amaral, O., Abualhaija, S., Torre, D., Sabetzadeh, M., & Briand, L. C. (2021). AI-enabled automation for completeness checking of privacy policies. arXiv preprint arXiv:2106.05688. org
- Singh, A. K., & Gupta, R. (2022). A privacy-preserving model based on differential approach for sensitive data in cloud environment. arXiv preprint arXiv:2212.12534. org
- Xu, R., Baracaldo, N., & Joshi, J. (2021). Privacy-preserving machine learning: Methods, challenges and directions. arXiv preprint arXiv:2108.04417. org
- Gruschka, N., Mavroeidis, V., Vishi, K., & Jensen, M. (2018). Privacy issues and data protection in big data: A case study analysis under GDPR. arXiv preprint arXiv:1811.08531. org
- Gupta, R., Saxena, D., & Singh, A. K. (2021). Data security and privacy in cloud computing: Concepts and emerging trends. arXiv preprint arXiv:2108.09508. org
- Sudhakar Tiwari, “AI-Driven Approaches for Automating Privileged Access Security: Opportunities and Risks”, International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 11, pp.c898-c915, November 2021, Available at :http://www.ijcrt.org/papers/IJCRT2111329.pdf
- Amazon Web Services. (2018). Data residency whitepaper. Retrieved from https://d1.awsstatic.com/whitepapers/compliance/Data_Residency_Whitepaper.pdf awscloud.com
- Amazon Web Services. (2018). Navigating GDPR compliance on AWS. Retrieved from https://d1.awsstatic.com/whitepapers/compliance/GDPR_Compliance_on_AWS.pdf