Best Practices for Implementing Oracle Cloud ERP Security in Industry
Abstract
The adoption of Oracle Cloud ERP (Enterprise Resource Planning) solutions has become increasingly prevalent across industries, streamlining business operations and data management. However, ensuring the security of sensitive financial and operational data in the cloud remains a top priority. This paper outlines industry best practices for implementing robust security measures within Oracle Cloud ERP deployments. Covering access control, data encryption, compliance, threat detection, and identity management, these recommendations offer a comprehensive guide to safeguarding critical business information and maintaining regulatory compliance.
References
Oracle. (2021). Oracle Cloud Security and Compliance Blueprint. Retrieved from https://www.oracle.com/security-cloud/blueprint.html
Chen, L., Zhang, Y., Zou, D., & Gong, X. (2020). A Study on Oracle Database Security and Encryption Technology. In 2020 3rd International Conference on Management Engineering, Software Engineering and Service Sciences (ICMSS).
Oracle. (2021). Oracle Identity and Access Management. Retrieved from https://www.oracle.com/security/cloud-identity-access-management.html
Deloitte. (2021). Oracle Cloud ERP: Transforming Finance and Accounting. Retrieved from https://www2.deloitte.com/us/en/pages/consulting/solutions/oracle-cloud-erp.html
PwC. (2021). Oracle Cloud Security: Reinventing the Art of the Possible. Retrieved from https://www.pwc.com/us/en/services/oracle.html
Kunduru, A. R., & Kandepu, R. (2023). Data archival methodology in enterprise resource planning applications (Oracle ERP, Peoplesoft). Journal of Advances in Mathematics and Computer Science, 38(9), 115–127. https://doi.org/10.9734/jamcs/2023/v38i91809
Mamza, E. S. (2021). Use of AIOT in Health System. International Journal of Sustainable Development in Computing Science, 3(4), 21–30.
Nadikattu, R. R. (2014a). Content analysis of American & Indian Comics on Instagram using Machine learning. International Journal of Creative Research Thoughts (IJCRT), ISSN, 2320–2882.
Kunduru, A. R. (2023). Artificial intelligence usage in cloud application performance improvement. Central Asian Journal of Mathematical Theory and Computer Sciences, 4(8), 42-47. https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/491
Kunduru, A. R. (2023). Artificial intelligence advantages in cloud Fintech application security. Central Asian Journal of Mathematical Theory and Computer Sciences, 4(8), 48-53. https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/492
Whig, P., Velu, A., & Naddikatu, R. R. (2022). The Economic Impact of AI-Enabled Blockchain in 6G-Based Industry. In AI and Blockchain Technology in 6G Wireless Network (pp. 205–224). Springer, Singapore.
Whig, P., Velu, A., & Nadikattu, R. R. (2022). Blockchain Platform to Resolve Security Issues in IoT and Smart Networks. In AI-Enabled Agile Internet of Things for Sustainable FinTech Ecosystems (pp. 46–65). IGI Global.
Whig, P., Velu, A., & Ready, R. (2022). Demystifying Federated Learning in Artificial Intelligence With Human-Computer Interaction. In Demystifying Federated Learning for Blockchain and Industrial Internet of Things (pp. 94–122). IGI Global.
Whig, P., Velu, A., & Sharma, P. (2022). Demystifying Federated Learning for Blockchain: A Case Study. In Demystifying Federated Learning for Blockchain and Industrial Internet of Things (pp. 143–165). IGI Global.
Kunduru, A. R. (2023). Cloud BPM Application (Appian) Robotic Process Automation Capabilities. Asian Journal of Research in Computer Science, 16(3), 267–280. https://doi.org/10.9734/ajrcos/2023/v16i3361
Kunduru, A. R. (2023). Machine Learning in Drug Discovery: A Comprehensive Analysis of Applications, Challenges, and Future Directions. International Journal on Orange Technologies, 5(8), 29-37.
Whig, P., & Ahmad, S. N. (2012f). Performance analysis of various readout circuits for monitoring quality of water using analog integrated circuits. International Journal of Intelligent Systems and Applications, 4(11), 103.
Whig, P., & Ahmad, S. N. (2013a). A novel pseudo-PMOS integrated ISFET device for water quality monitoring. Active and Passive Electronic Components, 2013.
Whig, P., & Ahmad, S. N. (2014a). Development of economical ASIC for PCS for water quality monitoring. Journal of Circuits, Systems and Computers, 23(06), 1450079.
Arjun Reddy Kunduru. (2023). From Data Entry to Intelligence: Artificial Intelligence’s Impact on Financial System Workflows. International Journal on Orange Technologies, 5(8), 38-45. Retrieved from https://journals.researchparks.org/index.php/IJOT/article/view/4727