In an era of expanding cross-domain data flow, algorithm-driven decision-making, and intelligent services, traditional data security solutions based on boundary protection and static authorization are no longer adequate for the governance needs of the big data era. This article, based on a review of international and industry research reports from recent years, analyzes the increasing cost of data breaches, stricter privacy regulations, the development of privacy-enhancing technologies, and the risks of secondary inference brought about by artificial intelligence. Research indicates that the main contradictions in data security and privacy protection currently lie in the conflict between releasing data value and the principle of minimum necessity, the conflict between the cost of privacy computing and business real-time requirements, and the imbalance between the black box nature of algorithms and accountability. Therefore, this paper proposes a comprehensive strategy system that combines classification and grading, zero-trust access, differential privacy, and federated learning, supported by a trusted execution environment, establishing complete on-chain auditing, and scoring governance capabilities. This research can provide a reference for government agencies, healthcare institutions, financial institutions, and platform enterprises to create data governance systems that balance security, compliance, and business value.

Yongqiang Ma. Research on Big Data Security Governance and Privacy Protection Strategies for Cross-Domain Data Circulation Scenarios. International Journal of Neural Network (2026), Vol. 5, Issue 1: 11-21. https://doi.org/10.38007/NN.2026.050102

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