ISSN : 2583-2646

Cloud Computing and Database Security: Strategies for Protecting Data Access in ERP Systems

ESP Journal of Engineering & Technology Advancements
© 2023 by ESP JETA
Volume 3  Issue 1
Year of Publication : 2023
Authors : Sanjay Ramdas Bauskar
:10.56472/25832646/JETA-V3I3P114

Citation:

Sanjay Ramdas Bauskar, 2023. "Cloud Computing and Database Security: Strategies for Protecting Data Access in ERP Systems", ESP Journal of Engineering & Technology Advancements 3(1): 200-208.

Abstract:

As organizations increasingly adopt cloud computing for enterprise resource planning (ERP) systems, ensuring robust database security and protecting data access have become paramount. Cloud environments, while offering flexibility, scalability, and cost-efficiency, also introduce new challenges in safeguarding sensitive business data. This paper explores key strategies for securing database access within cloud-based ERP systems, focusing on the vulnerabilities associated with cloud infrastructures and the potential risks to data integrity, confidentiality, and availability. We discuss various security mechanisms, including encryption, access controls, multi-factor authentication (MFA), and data masking, and evaluate their effectiveness in mitigating unauthorized access and data breaches. Additionally, the paper examines advanced techniques such as identity and access management (IAM), role-based access control (RBAC), and audit trails for monitoring and managing user activities in cloud-based ERP systems. Furthermore, the role of compliance frameworks, such as GDPR and HIPAA, in shaping security practices and ensuring regulatory compliance is analyzed. The paper concludes by offering recommendations for implementing a layered security approach that integrates these strategies, aiming to enhance the protection of data in cloud-based ERP environments and reduce the risk of cyber threats and data loss.

References:

[1] Avacharmal, R., Pamulaparthyvenkata, S., & Gudala, L. (2023). Unveiling the Pandora's Box: A Multifaceted Exploration of Ethical Considerations in Generative AI for Financial Services and Healthcare. Hong Kong Journal of AI and Medicine, 3(1), 84-99.

[2] Aravind, R. (2023). Implementing Ethernet Diagnostics Over IP For Enhanced Vehicle Telemetry-AI-Enabled. Educational Administration: Theory and Practice, 29(4), 796-809.

[3] Mahida, A. Explainable Generative Models in FinCrime. J Artif Intell Mach Learn & Data Sci 2023, 1(2), 205-208.

[4] Mandala, V., & Mandala, M. S. (2022). ANATOMY OF BIG DATA LAKE HOUSES. NeuroQuantology, 20(9), 6413.

[5] Perumal, A. P., Deshmukh, H., Chintale, P., Molleti, R., Najana, M., & Desaboyina, G. Leveraging machine learning in the analytics of cyber security threat intelligence in Microsoft azure.

[6] Kommisetty, P. D. N. K. (2022). Leading the Future: Big Data Solutions, Cloud Migration, and AI-Driven Decision-Making in Modern Enterprises. Educational Administration: Theory and Practice, 28(03), 352-364.

[7] Bansal, A. (2023). Power BI Semantic Models to enhance Data Analytics and Decision-Making. International Journal of Management (IJM), 14(5), 136-142.

[8] Laxminarayana Korada, & Vijay Kartik Sikha. (2022). Enterprises Are Challenged by Industry-Specific Cloud Adaptation - Microsoft Industry Cloud Custom-Fits, Outpaces Competition and Eases Integration. Journal of Scientific and Engineering Research. https://doi.org/10.5281/ZENODO.13348175

[9] Avacharmal, R., Sadhu, A. K. R., & Bojja, S. G. R. (2023). Forging Interdisciplinary Pathways: A Comprehensive Exploration of Cross-Disciplinary Approaches to Bolstering Artificial Intelligence Robustness and Reliability. Journal of AI-Assisted Scientific Discovery, 3(2), 364-370.

[10] Aravind, R., & Shah, C. V. (2023). Physics Model-Based Design for Predictive Maintenance in Autonomous Vehicles Using AI. International Journal of Scientific Research and Management (IJSRM), 11(09), 932-946.

[11] Mahida, A. (2023). Enhancing Observability in Distributed Systems-A Comprehensive Review. Journal of Mathematical & Computer Applications. SRC/JMCA-166. DOI: doi. org/10.47363/JMCA/2023 (2), 135, 2-4.

[12] Mandala, V. (2021). The Role of Artificial Intelligence in Predicting and Preventing Automotive Failures in High-Stakes Environments. Indian Journal of Artificial Intelligence Research (INDJAIR), 1(1).

[13] Perumal, A. P., Deshmukh, H., Chintale, P., Desaboyina, G., & Najana, M. Implementing zero trust architecture in financial services cloud environments in Microsoft azure security framework.

[14] Bansal, A. Advanced Approaches to Estimating and Utilizing Customer Lifetime Value in Business Strategy.

[15] Sikha, V. K., Siramgari, D., & Korada, L. (2023). Mastering Prompt Engineering: Optimizing Interaction with Generative AI Agents. Journal of Engineering and Applied Sciences Technology. SRC/JEAST-E117. DOI: doi. org/10.47363/JEAST/2023 (5) E117 J Eng App Sci Technol, 5(6), 2-8.

[16] Avacharmal, R., Gudala, L., & Venkataramanan, S. (2023). Navigating The Labyrinth: A Comprehensive Review Of Emerging Artificial Intelligence Technologies, Ethical Considerations, And Global Governance Models In The Pursuit Of Trustworthy AI. Australian Journal of Machine Learning Research & Applications, 3(2), 331-347.

[17] Ravi Aravind, Srinivas Naveen D Surabhi, Chirag Vinalbhai Shah. (2023). Remote Vehicle Access:Leveraging Cloud Infrastructure for Secure and Efficient OTA Updates with Advanced AI. EuropeanEconomic Letters (EEL), 13(4), 1308–1319. Retrieved fromhttps://www.eelet.org.uk/index.php/journal/article/view/1587

[18] Mahida, A. (2023). Machine Learning for Predictive Observability-A Study Paper. Journal of Artificial Intelligence & Cloud Computing. SRC/JAICC-252. DOI: doi. org/10.47363/JAICC/2023 (2), 235, 2-3.

[19] Perumal, A. P., & Chintale, P. Improving operational efficiency and productivity through the fusion of DevOps and SRE practices in multi-cloud operations.

[20] Bansal, A. (2022). Establishing a Framework for a Successful Center of Excellence in Advanced Analytics. ESP Journal of Engineering & Technology Advancements (ESP-JETA), 2(3), 76-84.

[21] Korada, L. (2023). AIOps and MLOps: Redefining Software Engineering Lifecycles and Professional Skills for the Modern Era. In Journal of Engineering and Applied Sciences Technology (pp. 1–7). Scientific Research and Community Ltd. https://doi.org/10.47363/jeast/2023(5)271

[22] Avacharmal, R. (2022). ADVANCES IN UNSUPERVISED LEARNING TECHNIQUES FOR ANOMALY DETECTION AND FRAUD IDENTIFICATION IN FINANCIAL TRANSACTIONS. NeuroQuantology, 20(5), 5570.

[23] Aravind, R., & Surabhii, S. N. R. D. Harnessing Artificial Intelligence for Enhanced Vehicle Control and Diagnostics.

[24] Mahida, A. (2022). Comprehensive Review on Optimizing Resource Allocation in Cloud Computing for Cost Efficiency. Journal of Artificial Intelligence & Cloud Computing. SRC/JAICC-249. DOI: doi. org/10.47363/JAICC/2022 (1), 232, 2-4.

[25] Chintale, P. (2020). Designing a secure self-onboarding system for internet customers using Google cloud SaaS framework. IJAR, 6(5), 482-487.

[26] Bansal, A. (2022). REVOLUTIONIZING REVENUE: THE POWER OF AUTOMATED PROMO ENGINES. INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING AND TECHNOLOGY (IJECET), 13(3), 30-37.

[27] Korada, L. (2023). Leverage Azure Purview and Accelerate Co-Pilot Adoption. In International Journal of Science and Research (IJSR) (Vol. 12, Issue 4, pp. 1852–1954). International Journal of Science and Research. https://doi.org/10.21275/sr23416091442

[28] Vehicle Control Systems: Integrating Edge AI and ML for Enhanced Safety and Performance. (2022).International Journal of Scientific Research and Management (IJSRM), 10(04), 871-886.https://doi.org/10.18535/ijsrm/v10i4.ec10

[29] Aravind, R., Shah, C. V & Manogna Dolu. AI-Enabled Unified Diagnostic Services: Ensuring Secure andEfficient OTA Updates Over Ethernet/IP. International Advanced Research Journal in Science, Engineeringand Technology. DOI: 10.17148/IARJSET.2023.101019

[30] Mahida, A. Predictive Incident Management Using Machine Learning.

[31] Chintale, P. SCALABLE AND COST-EFFECTIVE SELF-ONBOARDING SOLUTIONS FOR HOME INTERNET USERS UTILIZING GOOGLE CLOUD'S SAAS FRAMEWORK.

[32] Bansal, A. (2021). OPTIMIZING WITHDRAWAL RISK ASSESSMENT FOR GUARANTEED MINIMUM WITHDRAWAL BENEFITS IN INSURANCE USING ARTIFICIAL INTELLIGENCE TECHNIQUES. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND MANAGEMENT INFORMATION SYSTEMS (IJITMIS), 12(1), 97-107.

[33] Korada, L., & Somepalli, S. (2023). Security is the Best Enabler and Blocker of AI Adoption. In International Journal of Science and Research (IJSR) (Vol. 12, Issue 2, pp. 1759–1765). International Journal of Science and Research. https://doi.org/10.21275/sr24919131620

[34] Shah, C., Sabbella, V. R. R., & Buvvaji, H. V. (2022). From Deterministic to Data-Driven: AI and Machine Learning for Next-Generation Production Line Optimization. Journal of Artificial Intelligence and Big Data, 21-31.

Keywords:

Cloud Computing, Database Security, Data Access, ERP Systems, Cloud Infrastructure, Data Integrity, Data Confidentiality, Data Availability, Encryption, Access Controls, Multi-Factor Authentication (MFA), Data Masking, Identity and Access Management (IAM), Role-Based Access Control (RBAC), Audit Trails, User Activities, Compliance Frameworks, GDPR, HIPAA, Cyber Threats.