Sundarrajan Ramalingam
Periyar University, Salem, TN, India
Dr. Daksha Borada,
Assistant Professor
IILM University, Greater Noida
Abstract:
In today’s rapidly evolving digital landscape, ensuring data compliance and robust security measures within business intelligence (BI) ecosystems has become crucial, especially in the context of handling sensitive customer information. Business Intelligence systems are designed to analyze vast amounts of data to provide valuable insights for strategic decision-making. However, the increasing reliance on these systems for sensitive business operations brings a higher risk of data breaches, unauthorized access, and violations of data protection regulations.
This paper explores the importance of compliance and data security in BI ecosystems, focusing on how organizations can safeguard sensitive customer data while leveraging the power of analytics for informed decision-making. The research examines various compliance frameworks and regulatory standards, such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and Health Insurance Portability and Accountability Act (HIPAA), and how they impact BI infrastructure and data handling practices. It also highlights the importance of data security measures, including data encryption, access control, and audit trails, in protecting customer data from potential threats.
Furthermore, the paper delves into the challenges organizations face in balancing the need for data accessibility with stringent security requirements. The ever-growing volume of data, the shift to cloud-based platforms, and the increasing use of third-party services present additional complexities in maintaining compliance. This study discusses best practices for implementing data security protocols, including the use of advanced encryption techniques, role-based access control, and data masking, to ensure that only authorized personnel can access sensitive information.
Additionally, the paper addresses the significance of continuous monitoring and auditing within BI ecosystems. By integrating real-time security monitoring, organizations can detect and respond to potential threats proactively, minimizing the risk of data leaks and breaches. The research also emphasizes the need for a well-structured data governance framework that ensures adherence to compliance requirements and promotes accountability across all levels of the organization.
The study further explores the role of artificial intelligence (AI) and machine learning (ML) in enhancing data security within BI systems. AI-driven anomaly detection and predictive analytics can help identify patterns of suspicious activity and flag potential security threats before they materialize. The combination of AI and advanced security measures offers organizations a powerful approach to protecting sensitive customer data while maintaining a competitive edge in their BI initiatives.
Keywords: Data Compliance, Data Security, Business Intelligence, Sensitive Customer Information, GDPR, CCPA, HIPAA, Encryption, Access Control, Data Governance, AI, Machine Learning, Security Monitoring, Data Privacy.
References
- Alharby, M., & Drira, K. (2020). A comprehensive review of data privacy and security in cloud computing. International Journal of Computer Science and Information Security, 18(4), 93-102.
- Chaffey, D. (2019). Digital business and e-commerce management (7th ed.). Pearson Education.
- Chowdhury, S., & Talukder, M. A. H. (2019). Data security and privacy challenges in big data analytics. International Journal of Advanced Computer Science and Applications, 10(5), 242-249.
- Colijn, A. (2020). Ensuring data privacy in business intelligence environments. Journal of Information Privacy and Security, 16(2), 1-15. https://doi.org/10.1080/15536548.2020.1788994
- Gupta, A., & Chhabra, S. (2020). A review on data security and privacy for business intelligence systems. Procedia Computer Science, 167, 283-290. https://doi.org/10.1016/j.procs.2020.03.061
- He, H., & Zhang, Y. (2021). Privacy-preserving business intelligence: Challenges and solutions. Journal of Cloud Computing: Advances, Systems, and Applications, 10(3), 1-11. https://doi.org/10.1186/s13677-021-00257-7
- Kuo, M. H., & Lee, M. L. (2018). Cybersecurity in business intelligence and analytics platforms: A study of protection strategies. International Journal of Computer Applications, 182(2), 1-9.
- Lin, M., & Sun, Y. (2019). Ensuring compliance and security in data analytics systems: A survey. Security and Privacy, 2(1), 1-22. https://doi.org/10.1002/sec.127
- Liu, S., & Liu, Y. (2020). Securing data in business intelligence systems: A comprehensive survey. Journal of Information Security, 29(2), 89-103. https://doi.org/10.1016/j.jnca.2020.05.004
- Mena, A., & Matos, A. (2020). Privacy and compliance considerations in business intelligence frameworks. Computers & Security, 92, 101738. https://doi.org/10.1016/j.cose.2020.101738
- Moon, H., & Choi, W. (2018). Data security and compliance in cloud-based business intelligence systems: A practical approach. Cloud Computing and Security Issues in Big Data, 42(3), 255-262. https://doi.org/10.1007/978-3-319-95146-5_22
- Naseer, A., & Fatima, A. (2019). Securing data in cloud computing environments: A systematic review. International Journal of Cloud Computing and Services Science, 8(3), 202-213.
- Poongodi, M., & Manogaran, G. (2020). Security threats in data-driven business intelligence ecosystems. Journal of Big Data, 7(1), 34-50. https://doi.org/10.1186/s40537-020-00241-4
- Reddy, S. D., & Srinivas, P. (2021). Business intelligence systems and their security challenges: A review. Journal of Computer Science and Technology, 36(4), 737-748. https://doi.org/10.1007/s11390-021-0494-4
- Sharma, A., & Gupta, N. (2018). Exploring data security challenges in business intelligence ecosystems. Information Security Journal: A Global Perspective, 27(2), 77-89. https://doi.org/10.1080/19393555.2017.1413171
- Singh, J., & Dhillon, V. (2019). Privacy-preserving data analytics in business intelligence: A survey. Security and Privacy in Big Data, 1(1), 12-27. https://doi.org/10.1016/j.jbi.2018.12.001
- Soni, P., & Bhardwaj, A. (2020). Machine learning approaches for security and compliance in BI systems. Journal of Artificial Intelligence Research, 69, 233-249.
- Yadav, M., & Pahal, P. (2020). Data security and compliance measures for business intelligence applications. International Journal of Database Management Systems, 12(1), 33-49.
- Zhang, H., & Chen, T. (2020). Big data privacy, security, and compliance: A comprehensive analysis. Future Generation Computer Systems, 101, 1185-1194. https://doi.org/10.1016/j.future.2019.08.046
