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Data has become a strategic corporate asset, as vital as human resources and capital. Proprietary data, in particular, often represents a company¡¯s most critical competitive advantage. For in-house counsel, a thorough understanding of the competitive value of data and the legal mechanisms for its protection is essential to safeguarding long-term interests. This article analyses the evolving dynamics of data competition and explores trends in data litigation.

Proprietary data

In the digital economy, proprietary data has emerged as a key driver of corporate competitiveness. Proprietary customer data enables companies to ¡°know themselves and their competitors¡±, facilitating targeted customer acquisition while retaining existing ones.

Proprietary operational data allows businesses to monitor their performance, improving efficiency and forecasting trends. Industry-specific data provides a comprehensive perspective, offering strategic insights. In sectors such as technology, finance and retail, data advantages are often directly linked to market position. These datasets create a competitive moat.

Data litigation

Historically, data litigation has primarily targeted ¡°data scraping¡± and illicit ¡°black and grey market¡± activities. The commercial value of data itself was not independently recognised, but was instead indirectly protected as part of platform traffic interests.

With the development of the data circulation industry, alongside evolving policies, laws and tax systems, more companies are expected to initiate or become embroiled in new types of litigation concerning data circulation. In 2024, landmark IP cases from the Supreme People¡¯s Court such as Weimeng v Jian Yixun; Datatang Technology v Yinmu Technology; and AutoNavi Software v Wind Information saw plaintiffs shift their focus from traffic-related interests to the infringement of high-value data resources and the resulting transactional damages.

On this emerging battlefield, two notable new trends demand close attention.

1. Emergence of new offensive and defensive strategies driven by the value of data circulation. In AutoNavi Software v Wind Information, the plaintiff, represented by the authors, utilised virtual data licensing fee assessments to calculate damages, securing a first-instance award of RMB12.5 million (USD1.7 million). In the Datatang Technology v Yinmu Technology case, data product registration served as evidence of ownership, while open-source rules became a key basis for determining commercial ethics. These innovative strategies align with the characteristics of the data circulation industry, moving away from the traditional ¡°traffic economy¡± context. Proprietary data holders must track these developments.
2. Judicial focus on balancing interests in data circulation and preventing data silos. Simplistic standards such as free-riding and unearned gains have been abandoned, with prior rulings potentially overridden by specific scenarios in subsequent cases. Hasty actions often lead to unintended consequences. To gain an advantage, parties must adopt a highly cautious approach, thoroughly investigate evidence of infringement, invoke market rules, analyse interest relationships, argue competitive effects, and address key issues in balancing interests. Only through such meticulous preparation can they respond effectively.

Outlook in the AIGC era

The issue of balancing interests in data circulation has become a focal point of contention in the era of AI-generated content (AIGC), creating a new battleground for the protection of data rights. As the data elementisation process collides with the exponential evolution of AI technology, the already incomplete framework for data circulation rules and rights protection faces unprecedented structural challenges. This will provoke strong reactions from data holders.

AIGC copyright cases expose deep structural challenges in traditional rights frameworks. On one hand, proprietary data holders file infringement claims against AIGC products or users, while some argue that using copyrighted content to train AI constitutes fair use. On the other hand, AIGC creations frequently seek and obtain copyright protection. This contradiction highlights the inability of traditional copyright law to simultaneously promote AIGC development and safeguard high-quality human creativity.

The anxiety of proprietary data holders is more evident in this new battleground. The root cause lies in their diminishing control over data rights due to technological advancements, while the law has failed to provide complementary rules to restore such control. In data-related litigation, the burden of proof on proprietary data holders has significantly increased, requiring them to demonstrate illegal use or the unauthorised scraping or acquisition of data, which imposes high demands on companies and their legal teams.

AIGC often has no direct connection to the original data, leaving proprietary data holders with little more than suspicions that their data was used to train models or generate AIGC content, while the difficulty of evidence collection has escalated sharply. In this context, the flexible application of multi-jurisdictional and multi-departmental enforcement strategies may become the key to success.

Recommendations

Strengthen proprietary data protection. For high-value proprietary data, it is essential to improve legal frameworks for safeguarding data rights, enhance evidence collection capabilities and establish collaborative protection mechanisms with product and technology teams. This collaboration should focus on identifying infringement clues and proposing improvements to address business vulnerabilities. Efficient rights protection can support business growth, which in turn reinforces compliance and enforcement capabilities.

Enhance risk control in data-related operations. Many enterprises lack effective oversight of high-risk data-related activities in their business and technology operations. This has led to scenarios where technical personnel engage in shackled programming, and products or services are launched with inherent risks, exposing vulnerabilities. Therefore, enterprises should scrutinise data-related business and technical activities with the same rigour as they review contracts. They should adopt a combination of quantitative and qualitative methods to assess risks, ensuring risks are manageable, and red lines are not crossed.

Avoid blind spots in AI governance. While AI appears powerful and promising, it also conceals significant risks. Issues such as the legality of training data sources, the authenticity of ¡°open-source¡± claims for commercial AI products, the protection of enterprise algorithms and large models and the compliance of AI-based products must be given serious attention. Companies should shape data and AI rules to maintain control.

Only by leveraging a comprehensive strategy that combines business, technology, regulation and litigation can enterprises secure a competitive edge in this new battleground.

Zhao Kefeng is a partner at GEN Law Firm. He can be contacted by phone at +86 130 2122 4752 and by mail at Zhaokefeng@genlaw.com.
Zhang Hanxiong is an associate at GEN Law Firm. He can be contacted by phone at +86 176 0066 1870 and by mail at zhanghanxiong@genlaw.com.