As the digitalization process of enterprises accelerates, various application interfaces have become key hubs for system interconnection and data exchange. At the same time, they may also serve as entry points for attackers to obtain sensitive information. Without effective protection, they will face the risks of data leakage and business interruption. To address this issue, this paper proposes an automated risk identification method that integrates privacy computing and deep learning. By conducting semantic parsing and behavioral pattern mining on interface request data, a hybrid framework combining rule constraints and neural network feature extraction is constructed to achieve intelligent identification of abnormal requests and potential threats. This method utilizes vector representation, bidirectional recurrent networks, attention mechanisms, and multi-layer convolutional networks to optimize the output, effectively enhancing the model's risk prediction capability in complex environments. The developed interface security assessment system can dynamically identify attacks and sensitive data leaks, providing enterprises with efficient automated interface protection solutions. It also provides a reference for the application of privacy computing and deep learning in the security management of actual business.

Mingjie Chen. Research on Automated Risk Detection Methods in Machine Learning Integrating Privacy Computing. Machine Learning Theory and Practice (2025), Vol. 5, Issue 1: 108-116. https://doi.org/10.38007/ML.2025.050111.

[1] Yan H, Li X, Zhang W, et al. Automatic Evasion of Machine Learning-Based Network Intrusion Detection Systems[J]. Dependable and Secure Computing, IEEE Trans. on (T-DSC), 2024, 21(1):15. DOI:10. 1109/TDSC. 2023. 3247585.

[2] Mohanty S R, Maruf M U, Singh V, et al. Machine learning approaches for automatic defect detection in photovoltaic systems[J]. Solar Energy, 2025, 298. DOI: 10. 1016/j.solener.2025. 113672.

[3] Omer N, Samak A H, Taloba A I, et al. Cybersecurity Threats Detection Using Optimized Machine Learning Frameworks[J]. Computer Systems Science & Engineering, 2024, 48(1). DOI:10. 32604/csse. 2023. 039265.

[4] Sumalatha P, Mahalakshmi G S. DEF: DEEP ENSEMBLE NEURAL NETWORK CLASSIFIER FOR ANDROID MALWARE DETECTION[J]. international journal of computer networks and communications, 2024, 16(2):59-69.

[5] Jing, X. (2025). Research on the Application of Machine Learning in the Pricing of Cash Deposit Products. European Journal of Business, Economics & Management, 1(2), 150-157.

[6] Wu X, Bao W. Research on the Design of a Blockchain Logistics Information Platform Based on Reputation Proof Consensus Algorithm[J]. Procedia Computer Science, 2025, 262: 973-981.

[7] Sun S, Huang H, Payne E, et al. A graph embedding-based approach for automatic cyber-physical power system risk assessment to prevent and mitigate threats at scale[J]. IET Cyber-Physical Systems: Theory & Applications, 2024, 9(4). DOI:10. 1049/cps2. 12097.

[8] Li, W. (2025). Discussion on Using Blockchain Technology to Improve Audit Efficiency and Financial Transparency. Economics and Management Innovation, 2(4), 72-79.

[9] Xu Q. AI-Based Enterprise Notification Systems and Optimization Strategies for User Interaction[J]. European Journal of AI, Computing & Informatics, 2025, 1(2): 97-102.

[10] Su H, Luo W, Mehdad Y, et al. Llm-friendly knowledge representation for customer support[C]//Proceedings of the 31st International Conference on Computational Linguistics: Industry Track. 2025: 496-504.

[11] Z Zhong. AI-Assisted Workflow Optimization and Automation in the Compliance Technology Field [J]. International Journal of Advanced Computer Science and Applications (IJACSA), 2025, 16(10): 1-5.

[12] Huang, J. (2025). Research on Resource Prediction and Load Balancing Strategies Based on Big Data in Cloud Computing Platform. Artificial Intelligence and Digital Technology, 2(1), 49-55.

[13] Li W. Building a Credit Risk Data Management and Analysis System for Financial Markets Based on Blockchain Data Storage and Encryption Technology[C]//2025 3rd International Conference on Data Science and Network Security (ICDSNS). IEEE, 2025: 1-7.

[14] Wu Y. Graph Attention Network-based User Intent Identification Method for Social Bots[J]. Advances in Computer and Communication, 2025, 6(4).

[15] Wu Y. Software Engineering Practice of Microservice Architecture in Full Stack Development: From Architecture Design to Performance Optimization[J]. 2025.

[16] Sun Q. Research on Accuracy Improvement of Text Generation Algorithms in Intelligent Transcription Systems[J]. Advances in Computer and Communication, 2025, 6(4).

[17] Liu X. Emotional Analysis and Strategy Optimization of Live Streaming E-Commerce Users Under the Framework of Causal Inference[J]. Economics and Management Innovation, 2025, 2(6): 1-8.

[18] Lai L. Risk Control and Financial Analysis in Energy Industry Project Investment[J]. International Journal of Engineering Advances, 2025, 2(3): 21-28.

[19] Chen X. Research on Architecture Optimization of Intelligent Cloud Platform and Performance Enhancement of MicroServices[J]. Economics and Management Innovation, 2025, 2(5): 103-111.

[20] Yuan S. Application of Network Security Vulnerability Detection and Repair Process Optimization in Software Development[J]. European Journal of AI, Computing & Informatics, 2025, 1(3): 93-101.