This paper designs an artificial intelligence-based privacy security management system, focusing on analyzing data leakage and abnormal access risk prevention in enterprise IT operation and maintenance scenarios. The system adopts a modular architecture and deeply integrates technologies such as long short-term memory networks, temporal behavior modeling, convolutional neural networks, and log semantic feature extraction to intelligently analyze user operations, data flow paths, and permission invocation patterns in real time. By constructing dynamic privacy risk profiles, the system can accurately identify high-risk behaviors such as unauthorized access and covert data exfiltration. It significantly outperforms traditional rule-based methods in metrics such as anomaly detection accuracy and response latency, providing intelligent technical support for full lifecycle protection of privacy data.

Yan Yan. Design of an Artificial Intelligence-Based Privacy Security Management System. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 10: 63-67. https://doi.org/10.25236/AJCIS.2025.081009.

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