The training of big language models requires massive amounts of data support, from public web pages to book materials, with increasingly diverse sources of data. However, when personal privacy information, trade secrets, or copyrighted content is mixed into the training data of the model, a series of serious data security challenges also emerge.
Firstly, unintentional collection of personal data is the most common privacy issue. Many large models' training data comes from web crawlers, which inevitably contain personal identity information such as social media posts, forum comments, emails, etc. during the crawling process. Although developers typically perform data cleaning and anonymization, research has shown that specific personal information from training data can be "extracted" from the model through carefully designed prompts. This phenomenon is called "member inference attack", where attackers can determine whether an individual's information is being used for model training.
Secondly, the compliant use of copyrighted data is another core controversy. Between 2024 and 2026, multiple news organizations and content creators have filed copyright lawsuits against AI companies, accusing them of unauthorized use of copyrighted text for model training. The United States, the European Union, and China are actively exploring corresponding laws and regulations, attempting to find a balance between technological innovation and intellectual property protection.
Thirdly, the issue of data isolation in enterprise level AI applications cannot be ignored. Will sensitive business data be used for model training when enterprises input it into cloud based large models for processing? Is there a risk of data leakage? This requires companies to carefully evaluate the security commitments of model service providers and prioritize solutions that support private deployment or data encryption.
Faced with these challenges, the industry is forming multi-faceted response strategies. Differential privacy technology can add random noise during the training process to prevent the model from remembering individual data; Federated learning allows multiple parties to jointly train models without sharing raw data; And synthetic data technology can generate datasets that are similar in distribution to real data but do not contain real individual information. The comprehensive application of these technologies is building new defenses for data security in the AI era. Data privacy is not a stumbling block to the development of AI, but a cornerstone for achieving sustainable innovation.