At present, AIGC has become a hot topic in the fields of technology and business. At the 12th China Online Audiovisual Conference, AIGC attracted much attention, and its influence in the field of film and television creation expanded rapidly. For example, the adaptation efficiency of the film "The Journey of a Legendary Landscape Painting" was greatly improved with the help of AIGC. At the 2025 National Cultural and Creative Experimental Zone Investment Conference, numerous AIGC application projects were signed and implemented, such as the "AIGC Micro Drama Co Creation Plan", which will bring new changes to micro drama creation. All of these indicate that AIGC is rapidly integrating into various industries, bringing many development opportunities to enterprises. But there are also challenges behind the opportunities. With the expansion of AIGC application scenarios, legal compliance issues have become increasingly complex. Therefore, it is urgent to establish a sound legal risk prevention and control system. This article will analyze the compliance points and risk management strategies of AIGC service enterprises from the aspects of AIGC corpus collection, training, external service provision, compliance evaluation, etc., based on the "Basic Requirements for Security of Generative Artificial Intelligence Services" and the "Interim Measures for the Management of Generative Artificial Intelligence Services".

1、 Legal considerations and risk management for corpus security compliance

As the cornerstone of AIGC model training, the legality and security of corpus directly affect the legal risk status of the entire service. From the perspective of legal practice, enterprises must establish strict and comprehensive review mechanisms for language source control, while strengthening risk identification and response.

Enterprises must not take open source corpora lightly. There are various types of open source licensing agreements with complex terms, among which the contagious characteristics of copyleft terms require special attention. This feature means that if a company uses open-source corpus that follows the copyleft protocol, the products or services developed based on that corpus may also need to follow the same open-source protocol, which could have significant limitations on the company's commercial applications. In recent years, copyright disputes arising from violations of open source agreements such as GPL have become increasingly common in judicial practice. Before using open source corpus, enterprises must organize a professional legal team to thoroughly interpret the open source license agreement, clarify usage permissions and restrictions, and ensure that their behavior fully complies with the agreement provisions. At the same time, establish a risk monitoring mechanism for open source corpora, regularly review changes in authorization agreements for the use of open source corpora, adjust usage strategies in a timely manner, and avoid potential legal risks.

When self collecting data, enterprises need to accurately grasp the legal boundaries of data collection behavior. In addition to complying with the robotics protocol, which is a common practice in the industry, it is necessary to conduct in-depth research on the prohibitive provisions on data acquisition in Article 12 of the Anti Unfair Competition Law. In practical operation, it is recommended that enterprises establish a legal risk assessment mechanism before data collection. Before collecting any data, professional legal personnel should comprehensively and meticulously predict the legality of the collection behavior. At the same time, strengthen the monitoring of the collection process, record key information such as data sources, collection time, and collection methods, so that risks can be quickly traced and addressed.

The introduction of commercial language materials is an important component of language material sources, which also requires strict legal control and risk prevention. In the contract drafting process, in addition to specifying the standard quality clauses, key contents such as ensuring the legality of the corpus source and guaranteeing intellectual property defects should also be emphasized. Especially in cross-border data transactions, due to differences in data protection regulations across different countries and regions, companies need to be extra cautious. From the perspective of dispute resolution, it is crucial to clearly stipulate in the contract the remedies for breach of contract and the dispute resolution mechanism. In addition, establish a risk assessment system for commercial corpus suppliers, regularly evaluate the reputation, data quality, and compliance of suppliers, and reduce the risks caused by supplier issues.

Personal information protection plays a crucial role in corpus compliance. When processing corpora containing personal information, enterprises must strictly adhere to the principles established by the Personal Information Protection Law. For sensitive personal information, it is not only necessary to obtain the individual consent of the information subject, but also to conduct a specialized impact assessment in accordance with legal requirements. In the context of model training, the technical requirements for personal information de identification are more stringent. Enterprises need to invest corresponding technical resources and adopt advanced encryption, desensitization and other technical means to ensure the security of personal information during the training process and prevent information leakage and abuse. Establish an emergency response mechanism for personal information security incidents, which can quickly take measures in the event of a risk event such as information leakage, notify the information subject, report to regulatory authorities, and provide timely and effective remedies.

2、 Legal risk prevention and control and risk management of model compliance

As the core technology carrier of AIGC services, the compliance management and risk prevention of models need to be deeply considered from multiple legal dimensions.

Filing management is the primary step in model compliance. According to the Interim Measures for the Management of Generative Artificial Intelligence Services, services with public opinion attributes or social mobilization capabilities must complete safety assessments and filing procedures. From practical experience, the preparation of filing materials is relatively complicated, often involving technical specifications, management systems, security measures, and other aspects. Enterprises should plan ahead, develop detailed filing plans, clarify the responsible persons and time nodes for each link, ensure the smooth progress of filing work, and avoid delays in filing that may affect the normal operation of business. At the same time, we will continue to monitor changes in regulatory policies after filing, update filing information in a timely manner, and ensure that the model always meets regulatory requirements.

The dynamic control of output content is the core link of model compliance and risk management. To ensure the legality and security of the output content of the model, we suggest that enterprises establish a dual mechanism of "technical filtering+manual review". On the one hand, deploying advanced content security recognition systems and utilizing technologies such as natural language processing and image recognition to monitor and filter the output content of the model in real-time, promptly detecting and intercepting illegal and irregular content; On the other hand, establish a professional content review team to manually review high-risk content selected by the system, ensuring the accuracy and fairness of the review. The accuracy and reliability of model output content are crucial in specific service types such as automatic control, medical information services, and psychological counseling. Enterprises need to establish specialized risk assessment models to conduct multidimensional risk assessments on output content, including content accuracy, potential impact on users, etc. At the same time, clarify risk disclosure requirements and clearly inform users of potential risks, such as in medical information services, informing users that the information provided by the model cannot replace professional medical diagnosis and is for reference only.

Fulfilling transparency obligations is also an important aspect of model compliance and risk management. Starting from the requirements of the E-commerce Law, enterprises should fully disclose to users the functional characteristics, technological limitations, and usage of third-party models of their services. In terms of the specific form of information disclosure, it is recommended to adopt a hierarchical display approach, which prominently highlights important information such as service target audience, usage restrictions, potential risks, etc., while maintaining the simplicity of the overall interface to avoid user confusion caused by excessive information. For potential legal risks and liability limitation clauses, enterprises should prominently highlight them to ensure that users are clearly aware and avoid disputes over the effectiveness of the clauses due to insufficient prompts.

3、 System construction and risk response for compliant output content

The compliance of AIGC output content is directly related to the legal responsibility of enterprises, and enterprises must attach great importance to it and effectively respond to different risks.

In terms of value orientation, enterprises must ensure that the generated content complies with the basic principles established by the Cybersecurity Law. This requires enterprises to establish a content security evaluation mechanism during the algorithm design stage, and to monitor and evaluate the content generated by the model in real time through technical means such as rule engines and semantic analysis, in order to prevent the generation of content that violates socialist core values. In practical operation, the risk level of content varies in different fields, such as news and financial information. Due to their involvement in public interest and social stability, the risk level of content is relatively high, and stricter review standards need to be set. Enterprises should develop personalized audit strategies based on the characteristics of different fields to ensure the legality and compliance of the output content. At the same time, establish a public opinion monitoring mechanism to timely understand the feedback of the public on the output content, and adjust the model and review strategy in a timely manner when value orientation deviations are discovered.

The prevention of discriminatory content requires systematic solutions. From a legal perspective, this involves compliance requirements of multiple laws and regulations such as the Employment Promotion Law and the Law on the Protection of Women's Rights and Interests. It is recommended that enterprises start with training data screening to ensure the diversity and representativeness of data samples, and avoid discriminatory content in the model caused by data bias. At the same time, bias detection tools are deployed at the algorithmic level to regularly evaluate the diversity of output content and promptly identify and correct potential discriminatory issues. Especially in sensitive scenarios such as recruitment and credit, enterprises should establish a dedicated compliance review process, rigorously review the output content, and ensure that there is no discrimination against specific groups. Once discriminatory content is discovered, immediate measures should be taken to stop outputting, rectify the model, and apologize and compensate the affected groups.

The prevention and control of intellectual property infringement risks cannot be ignored. It is recommended that companies establish a multi-level defense system to address potential copyright issues. Firstly, establish a whitelist of copyrighted materials and prioritize the use of authorized materials to reduce the risk of infringement from the source; Secondly, deploy a content similarity detection system to screen the copyright of the output content and promptly identify content with high similarity to existing works; Finally, a rapid response mechanism for infringement complaints should be established to ensure that necessary measures can be taken promptly upon receiving infringement complaints, such as stopping infringement behavior, deleting infringing content, and compensating for losses. It should be noted that in some creative fields, there are still legal disputes over the copyright recognition of AI generated content itself. Enterprises should fully consider this factor in their business planning to avoid legal disputes caused by copyright issues.

In terms of specific service types, such as automatic control, medical information services, psychological counseling, etc., in addition to the general compliance measures mentioned above, clearer risk disclosure is also required. In the field of automatic control, enterprises should explain to users the possible control error risks of the model, as well as the system failure risks that may occur in abnormal situations; In the field of medical information services, it is explicitly stated that the information provided by the model does not have diagnostic authority, the results of use may differ from the actual condition, and excessive reliance on model information may delay the risk of illness; In the field of psychological counseling, emphasizing that model responses cannot replace face-to-face guidance from professional psychological counselors may not accurately understand users' complex emotional and psychological issues, and may even pose a risk of misleading users. By providing clear and explicit risk disclosure, users can fully understand the potential risks they may face before using the service, and make rational decisions accordingly.

4、 Improvement of compliance management system and risk management system

A sound compliance management system and risk management system are the fundamental guarantees for the stable development of AIGC enterprises. Enterprises should start from multiple aspects and continuously improve these two systems.

Establishing a full process compliance and risk assessment mechanism is the top priority. Enterprises should embed legal risk assessment and compliance review into every aspect of product development, operation, and maintenance, and develop detailed compliance and risk checklists that cover the entire lifecycle from corpus collection, model training, service provision, to subsequent maintenance. In terms of evaluation frequency, it is recommended to conduct a comprehensive evaluation at least once every quarter to promptly identify and address potential compliance and risk issues. At the same time, when there are significant updates to laws and regulations, special evaluations should be conducted in a timely manner to ensure that the business activities of the enterprise always comply with the latest legal requirements.

The standardized construction of complaint handling mechanisms is equally important. According to the requirements of the Consumer Rights Protection Law, enterprises should establish standardized complaint acceptance, investigation, and feedback processes. In the specific implementation process, enterprises need to pay special attention to the legal requirements of processing time limits to ensure effective handling of complaints within the prescribed time. At the same time, complete recording and preservation of complaint information should be carried out for subsequent analysis and summary. From the perspective of risk prevention, it is recommended that companies conduct regular analysis of complaint data, identify potential compliance risk points and user needs through data mining and analysis techniques, adjust relevant control measures in a timely manner, optimize products and services, and improve user satisfaction.

The emergency response capability of legal risks directly reflects the compliance and risk management level of enterprises. We suggest that companies refer to the relevant requirements of the Cybersecurity Law and develop practical and feasible emergency plans. The content of the contingency plan should include emergency response procedures for illegal and irregular content, temporary relief measures for user rights, and communication mechanisms with regulatory authorities. It is particularly noteworthy that contingency plans cannot be limited to the level of documents, and companies need to conduct regular drills to verify their actual effectiveness. During the drill, problems and deficiencies were identified in the contingency plan, and timely optimization and improvement were carried out to ensure that the enterprise can respond quickly and effectively in the face of sudden legal risks and security incidents, reducing losses.

conclusion

The compliance and risk management of AIGC technology is a long-term and arduous system engineering. With the deepening implementation of regulations such as the "Management Measures for Generative Artificial Intelligence Services", industry supervision will become increasingly strict. Enterprises should ensure the independence of compliance and risk management from an organizational structure, establish Chief Compliance Officer and Chief Risk Officer positions that report directly to senior management, and strengthen the coordination and coordination of compliance and risk management work. In terms of resource investment, it is necessary to strengthen the construction of internal legal teams and risk management teams, enhance the enterprise's own risk prevention and control capabilities, and timely introduce the support of external professional legal advisors and risk assessment institutions, leveraging professional strength to deal with complex legal issues and risk challenges.

Continuous legal training and corporate culture building are equally indispensable. Through regular training, enhance the compliance and risk awareness of all employees, and truly integrate compliance and risk management requirements into daily business processes, becoming a conscious action of employees. At the same time, enterprises should maintain a positive interaction with regulatory authorities, timely understand regulatory dynamics and policy guidance, actively adjust their own business, and ensure the stable development of enterprises on the track of compliance and controllable risks. In this rapidly developing field, only by deeply embedding compliance and risk management concepts into the genes of enterprises can they find the best balance between technological innovation and legal regulation, and achieve sustainable development.