With the deep penetration of AI technology in various industries, data privacy and security issues are becoming key bottlenecks restricting the further development of AI. In 2026, countries around the world will accelerate the promotion of AI legislation, and data security governance will shift from an "optional" to a "mandatory question".
At the training data level, large language models require massive amounts of text data for pre training, which inevitably contain personal information, sensitive content, and even copyrighted materials. In 2025, multiple copyright class action lawsuits against AI companies have caused industry shock, prompting companies such as OpenAI and Google to start signing data licensing agreements with news publishers and content platforms. At the same time, the EU AI Act will come into full effect in 2026, setting clear compliance requirements for training data of high-risk AI systems.
Federated learning, differential privacy, homomorphic encryption, and other privacy computing technologies are becoming key solutions to address the conflict between data sharing and privacy protection. Federated learning allows models to undergo collaborative training without sharing raw data, and has been implemented in fields such as healthcare and finance. For example, multiple hospitals use federated learning technology to jointly train disease diagnosis models, which not only protects patient privacy but also expands the coverage of training data.
The security vulnerabilities of AI systems are also worthy of attention. New threats such as data poisoning, adversarial attacks, and model inversion are emerging. At the end of 2025, security researchers discovered that "Prompt Injection attacks" targeting large language models can manipulate model outputs without directly accessing model weights, posing new security risks to enterprise level AI applications.
China is at the forefront of AI data governance in the world. The Interim Measures for the Management of Generative Artificial Intelligence Services will be officially implemented in 2023, requiring AI service providers to conduct security assessments of training data and establish a user complaint handling mechanism. In 2026, the Cyberspace Administration of China further released guidelines for AI data security assessment, which refined the data protection requirements for different types of AI systems.
For enterprises, it is urgent to establish a comprehensive AI data governance system. This is not only a compliance requirement, but also the foundation for winning user trust. From data collection, storage, processing to model training, deployment, and monitoring, privacy protection and security design need to be embedded in every step. The healthy development of AI relies on a secure and trustworthy data ecosystem. 【 Reference source 】 Comprehensive compilation of industry information released publicly. <｜end▁of▁thinking｜>
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