Data compliance is evolving from "issuing paper-based reports" to "full-process auditability": Shengyun Pandun uses AI agents to bridge the gap between legal opinions and technical solutions.
Targeting New Opportunities in Multi-jurisdiction Data Governance for Multinational Enterprises
In recent years, laws and regulations including the Data Security Law and the Personal Information Protection Law have been implemented, and the focus of regulatory verification has gradually shifted from the inspection of institutional completeness to the risk verification of the entire data life cycle. As domestic and overseas data compliance supervision continues to move toward penetrating inspection, enterprises' demands for data security are shifting from "issuing paper reports" to full-process governance that is implementable, auditable and sustainably operable. In highly regulated industries such as automotive, insurance and multinational manufacturing, where data assets are large in scale, business systems are complex and cross-border links are intertwined, the traditional compliance model that relies on manual sampling assessment has been difficult to meet the dual requirements of supervision and business.
In the scenario of global expansion, Chinese enterprises are facing a more complex compliance environment. GDPR, U.S. federal and state privacy laws and executive orders, and PDPA of ASEAN countries including Singapore form a multi-jurisdiction regulatory network. For GDPR alone, the maximum penalty for violations can reach 4% of the enterprise's total global annual revenue. For multinational enterprises with multiple factories, branches and business systems, the data flow path is complex, and cross-region, cross-entity and cross-platform data processing activities are difficult to be fully tracked by manual means.
At the same time, the deployment of enterprise private large models and AI agents is spawning new governance demands. Due to the requirements of sensitive information protection, more and more enterprises choose to deploy AI systems locally, but the subsequent issues such as AI asset registration, permission boundaries, data access and audit traces have not been fully covered by traditional data security tools. The advancement of rules such as domestic AI filing and the EU AI Act has also made risk governance of AI systems a new rigid demand for enterprise procurement.
DataAigis, with AI agents as the core, builds a data compliance governance platform integrating law and technology, trying to open up a complete closed loop from data asset identification, risk assessment, rectification and implementation to evidence retention.
Traditional Data Compliance Services Are Facing Breakpoints
The current supply of data compliance services is divided by capabilities: law firms and consulting institutions are responsible for interpreting legal rules and identifying risks, cybersecurity vendors are responsible for data scanning and monitoring, and AI technology vendors are responsible for models and tools. The three types of services have their own strengths, but for enterprises, how to translate legal requirements into rectification plans for specific systems, and how to map scanning results to regulatory provisions to form evidence that can be accepted by regulators, often falls in the gap between the three parties, which requires enterprises to connect them on their own.
The practical problem brought by this division of labor is that enterprises need to coordinate lawyers, consultants, security vendors and internal IT teams at the same time, resulting in slow project progress, vague responsibility boundaries, and a gap between compliance investment and actual effect.
What DataAigis tries to cut into is exactly the gap between legal capabilities and technical capabilities. Its core idea is not to simply superimpose AI on the compliance process, but to reconstruct the data compliance workflow through AI agents, allowing machines to undertake more repetitive identification, scanning, classification and monitoring work, and letting legal experts focus on high-risk judgment and professional conclusions.
AI Agent Drives Full-Link Governance
DataAigis's products are centered on the DataAigis platform, with sub-systems including data security compliance governance system, privacy and AI governance system, and AI agent management system, which can be deployed independently or delivered in combination to form integrated capabilities from data asset inventory to AI system governance.
At the level of data security compliance governance, the platform uses AI agents to complete automatic discovery of data assets, classification and grading of sensitive fields, risk assessment, cross-border traffic monitoring and archiving of audit evidence. For large enterprises, data assets are scattered in different business systems, databases, interfaces and cloud environments, and traditional manual inventory can only cover limited samples. AI agents can continuously scan and identify the distribution of sensitive data, compressing the manual assessment cycle that originally takes several months to a shorter period, and improving risk coverage.
At the level of privacy and AI governance, the platform builds compliance management capabilities around enterprise large models and agent applications, including AI asset registration, data subject request management, compliance obligation list, and permission boundary control. This capability aims to help enterprises meet the requirements of rules such as domestic AI filing and the EU AI Act, and reduce the compliance uncertainty after the introduction of AI systems.
At the level of AI agent management, the platform emphasizes local deployment and unified governance. Enterprises can deploy digital employees and business agents in a private environment, and the platform is responsible for unified account permissions, data access boundaries, operation audits and risk traces. Different from many large model services charged by seats, DataAigis adopts a project-based delivery method, which is more suitable for the requirements of large enterprises for cost control and data isolation.
From Project Delivery to Continuous Compliance
In terms of business model, DataAigis adopts a combination of "assessment and rectification project + annual continuous compliance service + platform authorization". Customers usually start with initial data compliance diagnosis or assessment and rectification projects, and after completing risk identification and rectification suggestions, gradually turn to annual continuous compliance services and platform-based deployment.
The advantage of this path is that it lowers the threshold for customers' first procurement. For enterprises, data compliance projects often involve multi-department collaboration, and it is necessary to clarify the risk base and rectification priority in the early stage. Through short-term assessment projects, enterprises can quickly obtain data maps, risk lists, rectification baselines and evidence packages, and then expand long-term monitoring and platform capabilities according to actual needs.
In insurance industry projects, DataAigis once assisted customers in evaluating a large number of business systems and sensitive fields, compressing the traditional long-cycle manual project to a shorter time, and emphasizing full-scan and risk closed loop. In the scenario of multinational manufacturing, its solution involves multiple overseas factories, multi-jurisdiction compliance requirements and long-term renewal services, which reflects the possibility of transformation from project system to continuous service.
Automobile manufacturing, financial insurance and multinational manufacturing are the key industries that DataAigis is currently focusing on. These industries have high data sensitivity, strong regulatory requirements, and complex business systems, which put forward higher requirements for the depth of compliance systems and delivery capabilities, and therefore have stronger willingness to pay and higher user stickiness.
Typical Case: Multi-jurisdiction Compliance Implementation of Connected Car Products for Global Expansion
An intelligent connected vehicle enterprise targeting the European and American markets continuously generates user data through on-board terminals, user Apps and after-sales service systems. Its architecture is a typical form of global expansion: European and American user data is stored in European cloud regions, and Chinese R&D and customer service teams need remote access. The questions the enterprise needs to answer are very specific: whether this architecture is valid under the respective privacy rules of the EU and the United States, in what ways can domestic teams still access data, and what documents and processes need to be supplemented for vehicle networking functions and Apps in different markets. These issues span legal, product R&D and operation, which need to be sorted out at one time and implemented into business processes.
The enterprise finally chose DataAigis to undertake this data compliance consulting project, as it values that half of such problems are legal problems and the other half are architectural problems: texts such as privacy policies and user agreements need to be issued by lawyers, while data storage areas, remote access methods and cloud configurations need engineers to give executable solutions. Lawyers and engineers are in the same team, which means that the enterprise can get two types of conclusions in one project, without having to connect legal opinions and technical solutions by themselves.
The project is promoted by DataAigis's consultant and lawyer team in four stages: data diagnosis, legal framework, cross-border architecture and compliance operation. First, draw a data map to sort out the data flow and cross-border access relationship of on-board terminals, user Apps, after-sales and third-party components; then form a compliance gap report based on business interviews in Europe and the United States, and convert the gaps into executable rectification actions item by item; in view of the current situation of European cloud region storage and Chinese remote access, give compliance suggestions on cloud security strategy and cross-border data transmission architecture; at the legal level, lawyers issue compliance guidelines, privacy policies, user agreements, DPA, Cookie policies, as well as SOPs for data subject requests and incident response. The project delivered 12 types of achievements and 13 documents in total.
After the completion of the project, the enterprise has a clear view of the data flow and cross-border access relationship of vehicles, Apps and third-party components. The legal and technical teams carry out collaborative rectification based on the same set of basis, and obtain the documents and operation basis required for subsequent policy maintenance, data subject request processing and security incident response.
Compound Founding Team Builds Differentiated Underlying Barriers
The data compliance track has multiple attributes of law, engineering and industry implementation, and the cross-domain compound capability of the team is the core foundation for project implementation. DataAigis's founding team is composed of members with backgrounds in industrial technology, multinational compliance and large-scale customer commercialization. Chairman Hao Junsheng holds EMBA degrees from CEIBS and Tsinghua University PBC School of Finance, he once served as Vice President and CTO of Digital China, founder of Shanghai Yunjiao (the company was acquired by Digital China for 360 million yuan), and has many years of experience in enterprise digitalization, industrial M&A and large enterprise service management. CEO and CTO Zhao Rui holds a master's degree from the University of Manchester, has more than 20 years of enterprise-level software R&D experience, has worked in Microsoft, Shanghai Yunjiao and Digital China successively, and led the design and iteration of the underlying AI agent engine of the platform. COO Xuan Xian is a master of Berkeley Law School, holds lawyer practice qualifications in China and the United States, once served as Asia Pacific legal counsel of Palo Alto Networks, is familiar with global multi-regional data privacy regulations, and is responsible for structuring and digitizing complex legal rules into a rule engine executable by the platform AI.