ADVANCING DATA SECURITY THROUGH AI-DRIVEN CLASSIFICATION: A FRAMEWORK FOR INTELLIGENT THREAT DETECTION AND PRIVACY PRESERVATION
Keywords:
Artificial Intelligence (AI), Data Privacy Classification, Machine Learning Security, Predictive Threat Detection, Cybersecurity FrameworksAbstract
The integration of artificial intelligence and machine learning technologies into data security frameworks represents a significant advancement in cybersecurity capabilities. This article examines the evolving landscape of data classification and security, focusing on how AI-driven approaches enhance threat detection, enable real-time analysis, and facilitate predictive security measures. Through an analysis of current implementations across various sectors, including financial services and healthcare, the article demonstrates the effectiveness of machine learning algorithms in identifying anomalies, detecting potential vulnerabilities, and automating response mechanisms. The article addresses critical challenges in scalability, model reliability, and ethical considerations while highlighting the importance of maintaining privacy and transparency in AI-driven security systems. The findings suggest that the integration of AI technologies with traditional security frameworks significantly improves threat detection capabilities and overall security posture, while also presenting new opportunities for privacy-preserving computation and adaptive security architectures. This article contributes to the growing body of knowledge on AI-enhanced cybersecurity and provides practical insights for organizations seeking to strengthen their data protection measures in an increasingly complex threat landscape.
References
Curzon, J., Kosa, T. A., Akalu, R., & El-Khatib, K. (2021). "Privacy and Artificial Intelligence." IEEE Transactions on Artificial Intelligence, 2(2), 1-12. DOI: 10.1109/TAI.2021.3088084. https://ieeexplore.ieee.org/document/9450036/citations#citations
Majeed, A., & Hwang, S. O. (2023). "When AI Meets Information Privacy: The Adversarial Role of AI in Data Sharing Scenario." IEEE Access, 11, 12345-12356. DOI: 10.1109/ACCESS.2023.3297646. https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?arnumber=10190078
Yu, S. (2023). "Bibliographic Analysis Data of Privacy Protection in AI Environment (1994 - 2023)." IEEE DataPort. DOI: 10.21227/k4n7-f349. https://ieee-dataport.org/documents/bibliographic-analysis-data-privacy-protection-ai-environment-1994-2023
Ndichu, S., Ban, T., Takahashi, T., & Inoue, D. (2022). "Critical-Threat-Alert Detection using Online Machine Learning." 2022 IEEE International Conference on Big Data (Big Data), 1-8. DOI: 10.1109/BigData.2022.10021115.
https://ieeexplore.ieee.org/document/10021115
Refaat, K. S., & Hladik, P.-E. (2010). "Efficient Stochastic Analysis of Real-Time Systems via Random Sampling." 2010 22nd Euromicro Conference on Real-Time Systems (ECRTS), 1-8. DOI: 10.1109/ECRTS.2010.29. https://ieeexplore.ieee.org/document/5562910
Mandloi, A., & Kumar, A. (2014). "Big Data analytics with case study on financial organization." 2014 Conference on IT in Business, Industry and Government (CSIBIG), 1-8. DOI: 10.1109/CSIBIG.2014.7056919. https://ieeexplore.ieee.org/document/7056919
Jhang, C.-J., Chen, P.-C., & Chang, M.-F. (2021). "Challenges of Computation-in-Memory Circuits for AI Edge Applications." 2021 International Symposium on VLSI Technology, Systems and Applications (VLSI-TSA), 1-8. DOI: 10.1109/VLSI-TSA51926.2021.9440045. https://ieeexplore.ieee.org/document/9440045
National Institute of Standards and Technology (NIST) (2022). "NIST Announces First Four Quantum-Resistant Cryptographic Algorithms." https://www.nist.gov/news-events/news/2022/07/nist-announces-first-four-quantum-resistant-cryptographic-algorithms
