AI-Driven Approaches to Database Security and Disaster Recovery: Enhancing Resilience and Threat Mitigation
Abstract
In today's rapidly evolving digital landscape, ensuring the security and resilience of databases in cloud environments is paramount. Traditional methods of database security and disaster recovery are often insufficient to address the growing sophistication of cyber threats and system failures. This paper explores AI-driven approaches to enhancing database security and disaster recovery processes. By leveraging machine learning, predictive analytics, and automation, organizations can proactively detect security vulnerabilities, mitigate threats, and recover from disasters more efficiently. AI algorithms can identify anomalous patterns in database activity, predict potential failures, and automate recovery actions, reducing downtime and minimizing the impact of security breaches. This paper discusses the role of AI in strengthening database resilience, the integration of AI with existing security frameworks, and the application of intelligent disaster recovery strategies to ensure business continuity in the face of unforeseen events.
References
Anderson, R., & Moore, T. (2019). Security engineering: A guide to building dependable distributed systems (3rd ed.). Wiley.
Bace, R. G. (2018). Intrusion detection. Pearson Education.
Bhattacharyya, S., & Sanyal, S. (2020). Machine learning for database security: A review. Journal of Computer Science and Technology, 35(2), 241-257.
Chen, L., & Zhang, W. (2017). Artificial intelligence for cybersecurity: A survey. Springer.
Chopra, S., & Sharma, P. (2021). A survey on AI-based anomaly detection techniques for database security. Journal of Information Security, 8(4), 118-128.
Das, S., & Patnaik, S. (2020). AI in cybersecurity: Threat detection and mitigation. Elsevier.
Dube, R., & Gupta, S. (2021). AI-driven disaster recovery: A modern approach. International Journal of Cloud Computing and Services Science, 10(3), 55-63.
Gupta, A., & Bansal, R. (2022). Machine learning in database management systems: A review of current trends. Journal of Computer Applications, 44(2), 112-124.
Hamilton, J., & Walker, D. (2019). Cloud security and privacy: An enterprise perspective on risks and compliance. O'Reilly Media.
Hsieh, Y., & Yang, C. (2020). AI-based security for cloud databases: A comprehensive survey. Cloud Computing, 7(2), 75-92.
Jain, A., & Gupta, R. (2021). Predictive analytics in database security: Applications and challenges. International Journal of Security and Privacy, 15(3), 87-98.
Kumar, V., & Singh, P. (2022). Artificial intelligence in disaster recovery: Applications and future trends. Springer.
Li, X., & Zhang, Y. (2018). Enhancing database security using machine learning algorithms. Journal of Database Management, 29(4), 78-92.
Miao, L., & Li, J. (2020). A survey of AI-based database security systems. Computer Science Review, 34, 23-35.
Nair, S., & Raj, S. (2021). AI-driven disaster recovery for cloud-based databases. Wiley-Blackwell.
Patel, H., & Kumar, R. (2020). The role of AI in disaster recovery systems for databases. Journal of Cloud Computing and Data Security, 9(1), 42-55.
Raj, K., & Chandra, R. (2019). AI-based anomaly detection for database security: A comparative study. International Journal of Information Security, 28(4), 102-113.
Sharma, A., & Yadav, M. (2021). AI and machine learning for database management and security. CRC Press.
Wang, Y., & Liu, Z. (2020). Enhancing disaster recovery with AI-based predictive analytics. Journal of Cloud Computing, 12(3), 128-142.
Zhang, Y., & Liu, X. (2019). Artificial intelligence in cloud computing and database management. Springer.
