Enterprise AI Transformation Data Compliance Risks and Responses: A Legal Practice Guide in the Context of the 2026 ITValue Summit Outlook

Lead: Calm Legal Reflection on the AI Implementation Boom

The 2026 ITValue Summit Forward-Looking Series livestream turns its lens to the path of enterprises' digital-intelligent leap from digitalization to AI, focusing on the practices and challenges of AI system implementation. As enterprises rush to introduce large models, deploy intelligent customer service, and optimize supply chain forecasting, a critical issue is often overlooked: every step of AI system implementation is accompanied by data collection, algorithm-driven decision-making, model training, and other processes that are increasingly subject to strict constraints from laws and regulations such as the Data Security Law, the Personal Information Protection Law, and the Provisions on the Administration of Algorithmic Recommendations. As lawyers handling corporate legal matters, we have seen far too many enterprises suffer administrative penalties, trade secret leaks, and even criminal risks due to data compliance flaws. This article will deconstruct the core legal issues in enterprise AI transformation from a legal professional perspective, providing actionable compliance pathways for enterprises nationwide.

Legal Boundaries of Data Collection and Processing

The first step in an enterprise AI system is often data collection. Whether it is user behavior data, business operations data, or third-party purchased data, it must be ensured that the sources are lawful and the authorization is clear. Article 13 of the Personal Information Protection Law explicitly requires that obtaining individual consent is necessary for processing personal information, and the minimum necessary principle must be followed. In practice, many companies obtain user authorization at once through standard form clauses, but the data dimensions required by AI models may far exceed the original scope of authorization. In such cases, authorization must be obtained anew or anonymization processing must be carried out.

Take a certain retail enterprise as an example. Its AI membership system collected consumers' shopping records and facial recognition data without separately obtaining explicit consent for biometric information, and was ultimately imposed a hefty fine by the regulatory authority pursuant to Article 66 of the Personal Information Protection Law. We recommend that enterprises establish a legality review mechanism for data sources at the design stage of AI data collection, distinguish between personal information, sensitive personal information, important data, and general commercial data, and formulate differentiated processing procedures. At the same time, where cross-border data transfer is involved, enterprises must also comply with the requirements of the Measures on the Security Assessment of Data Outbound Transfer and fulfill security assessment, standard contract, or certification procedures.

Requirements for Transparency and Fairness in Algorithmic Decision-Making

The core of AI systems lies in algorithms, and the algorithm black box is becoming a high-incidence area of legal risks. The Provisions on the Administration of Algorithm Recommendation in Internet Information Services require algorithm recommendation service providers to uphold mainstream value orientation and establish sound management systems such as algorithm mechanism review and technology ethics review. Furthermore, the Measures for the Labeling of Artificial Intelligence-Generated Synthetic Content, implemented in 2025, require explicit labeling of AI-generated content.

In practice, we have encountered a fintech company whose AI risk control model, due to historical biases in its training data, systematically rejected loan applications from users in certain regions, leading to collective complaints and regulatory scrutiny. Under Article 18 of the E-commerce Law, where e-commerce operators provide search results for goods or services based on consumer characteristics such as interests, preferences, or consumption habits, they must simultaneously offer options that are not tailored to the individual's characteristics. This means that enterprises using algorithms for personalized recommendations or automated decision-making must ensure transparency and explainability. Lawyers recommend that enterprises establish algorithm impact assessment mechanisms, regularly review whether algorithmic decision outcomes contain discriminatory bias, and retain human review channels to ensure that users have the right to challenge automated decisions.

Intellectual Property and Trade Secret Protection of AI Training Data

Enterprise AI model training typically relies on massive amounts of data, which may include copyright-protected text, images, or trade secrets. If training data is used without authorization, it may constitute infringement. In a case involving an AI painting platform heard by the Beijing Internet Court in 2024, the platform was found to have committed infringement and was ordered to pay damages because its training data included artistic works copyrighted by others. This case has served as a wake-up call for AI companies nationwide: lawful authorization of training data is a prerequisite for AI commercialization.

We recommend that enterprises, when procuring training data, should clarify the legality of data sources and intellectual property ownership through contracts; if using public data, they need to review whether its terms of use permit use for machine learning; for the enterprise's own trade secrets, confidentiality measures should be established during the AI development process to prevent model outputs from causing trade secret leakage. For example, a manufacturing enterprise used customer lists and pricing data for AI sales forecasting, but because it failed to desensitize model outputs, core customer information was leaked through AI-generated reports, triggering a trade secret infringement lawsuit. Lawyers can assist enterprises in establishing comprehensive AI data lifecycle management standards, from collection, storage, use, to destruction, ensuring that every stage complies with the requirements for trade secret protection under the Anti-Unfair Competition Law.

Contract and Liability Allocation Strategies for Enterprise AI Transformation

AI system deployment often involves multiple parties: AI suppliers, cloud service providers, data providers, consulting firms, and others. Once a system malfunction, algorithm error, or data breach occurs, how should liability be allocated? Article 1165 of the Civil Code stipulates that a person who through fault infringes upon the civil rights and interests of another, causing damage, shall bear tort liability. However, the complexity of AI systems makes fault determination difficult.

In practice, we recommend that enterprises clearly stipulate core clauses when signing AI service contracts, including service level agreements (SLA), data security responsibilities, algorithm audit rights, intellectual property ownership, and caps on damages. For example, after a logistics company introduced an AI dispatch system, defective algorithms from the supplier led to incorrect delivery routes, causing significant losses. Because the contract did not clearly define responsibility for algorithm accuracy, the two parties were locked in disputes over compensation. Lawyers should assist enterprises in reviewing exculpatory clauses and limitation of liability provisions in contracts to ensure that enterprises can effectively pursue accountability when AI systems pose compliance risks. At the same time, enterprises should establish a legal compliance review process for AI projects, incorporating legal assessments at each stage of project initiation, development, testing, and deployment, thereby front-loading compliance costs and avoiding the high expense of post-hoc remediation.

Conclusion: Compliance is the cornerstone of AI transformation.

Corporate digital-intelligent transformation is not merely a technological upgrade, but a systematic change involving law, ethics, and management. From data collection to algorithmic decision-making, from intellectual property to contractual liability, legal risks are hidden in every link. If companies can embed compliance awareness into the top-level design of their AI strategy, they can not only avoid administrative penalties and litigation risks, but also win customer trust and market competitiveness. As lawyers deeply engaged in the field of technology law, we recommend that companies conduct regular AI compliance audits, establish cross-departmental compliance teams, or retain professional lawyers to provide full-process legal support. Guangdong Zhiming Law Firm focuses on data compliance, intellectual property, and dispute resolution in corporate digital transformation, and has provided legal risk assessments and compliance solution designs for AI projects to multiple nationwide enterprises. If your company is at a critical juncture of AI transformation, we welcome you to discuss with us and let the law safeguard technological innovation.