A Cloud-Based Blockchain and AI Hybrid Model for Secure CRM Data Management in Salesforce
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V5I2P113Keywords:
Blockchain, Artificial Intelligence, Cloud Computing, Salesforce CRM, Data Security, Smart ContractsAbstract
Scalability, flexibility and integration capability of cloud-based Customer Relationship Management (CRM) solutions like Salesforce have become significant to the operations of the enterprise. Nonetheless, centralized cloud nature of CRM systems also creates serious concerns to do with data security, privacy, trust and regulatory compliance especially in a multi-tenancy scenario where sensitive customer information are often accessed and shared. The current security measures are largely based on controls that are located at the perimeter and are also based on static access policy-based controls that are not adequate to handle insider threats, tampering of data, and advanced persistent attacks. To handle these issues, this paper introduces a hybrid credit model of Blockchain and Artificial Intelligence (AI) based on cloud computing to manage CRM information in Salesforce. The suggested framework will combine a permissioned blockchain layer to facilitate data integrity, immutability, transparent auditing, and decentralized trust and an AI-provided security layer will be an intelligent anomaly detector, behavioral analytics, and predictive access control. The multi-layer architecture is developed and deployed, which includes smart contracts to handle a fine-grained access policy and machine learning algorithms to detect threats in real time. The metrics used to determine the evaluation of the model include security, performance, and scalability of the model such as data integrity verification time, access latency, throughput and detector accuracy. Experimental evidence by showing improved data security and trust using the proposed hybrid approach without having a prohibitive performance overhead and allowing it to scale to the size of an enterprise CRM deployment. The most valuable contribution is that the paper introduces an effective and scalable security model that integrates blockchain and AI in a chain of strength cloud-based CRM systems, which can be seen as a strong solution to reliable, intelligent, and secure CRM data management.
References
[1] Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., ... & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58
[2] Pearson, S. (2012). Privacy, security and trust in cloud computing. In Privacy and security for cloud computing (pp. 3-42). London: Springer London.
[3] Sareddy, M. R. (2023). Cloud-based customer relationship management: Driving business success in the e-business environment. International Journal of Marketing Management, 11(2), 58-72.
[4] Sharma, P. K., Kaushik, P. S., Agarwal, P., Jain, P., Agarwal, S., & Dixit, K. (2017, October). Issues and challenges of data security in a cloud computing environment. In 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) (pp. 560-566). IEEE.
[5] Crosby, M., Pattanayak, P., Verma, S., & Kalyanaraman, V. (2016). Blockchain technology: Beyond bitcoin. Applied innovation, 2(6-10), 71.
[6] Christidis, K., & Devetsikiotis, M. (2016). Blockchains and smart contracts for the internet of things. IEEE access, 4, 2292-2303.
[7] Androulaki, E., Barger, A., Bortnikov, V., Cachin, C., Christidis, K., De Caro, A., ... & Yellick, J. (2018, April). Hyperledger fabric: a distributed operating system for permissioned blockchains. In Proceedings of the thirteenth EuroSys conference (pp. 1-15).
[8] Mell, P., & Grance, T. (2011). The NIST definition of cloud computing.
[9] Wei, P., Wang, D., Zhao, Y., Tyagi, S. K. S., & Kumar, N. (2020). Blockchain data-based cloud data integrity protection mechanism. Future Generation Computer Systems, 102, 902-911.
[10] Sukhodolskiy, I., & Zapechnikov, S. (2018, January). A blockchain-based access control system for cloud storage. In 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) (pp. 1575-1578). IEEE.
[11] Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2017, June). An overview of blockchain technology: Architecture, consensus, and future trends. In 2017 IEEE international congress on big data (BigData congress) (pp. 557-564). IEEE.
[12] Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1, No. 2). Cambridge: MIT press.
[13] Elekar, K. S. (2015, September). Combination of data mining techniques for intrusion detection system. In 2015 international conference on computer, communication and control (IC4) (pp. 1-5). IEEE.
[14] Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 1-58.
[15] Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018). A deep learning approach to network intrusion detection. IEEE transactions on emerging topics in computational intelligence, 2(1), 41-50.
[16] Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19-31.
[17] Rose, S., Borchert, O., Mitchell, S., & Connelly, S. (2020). Zero trust architecture. NIST special publication, 800(207), 1-52.
[18] Salesforce Data Cloud is a Game Changer, Datatoolspro, Online. https://datatoolspro.com/salesforce-data-cloud-is-a-game-changer/
[19] Huang, P., Fan, K., Yang, H., Zhang, K., Li, H., & Yang, Y. (2020). A collaborative auditing blockchain for trustworthy data integrity in cloud storage system. IEEE Access, 8, 94780-94794.
[20] Patel, J., & Chouhan, A. (2016, October). An approach to introduce basics of Salesforce. com: A cloud service provider. In 2016 International Conference on Communication and Electronics Systems (ICCES) (pp. 1-8). IEEE.
[21] Wang, C. H., & Lien, C. Y. (2019). Combining design science with data analytics to forecast user intention to adopt customer relationship management systems. Journal of Industrial and Production Engineering, 36(4), 193-204.
