Why Restrict Generative AI Services Based on Corporate Registration and Control Relationships?

In recent years, regional policies for generative AI services have expanded from asking “where is the user located?” to examining who controls the company, how the service is used, and how data and models move across borders. Based on currently available policy descriptions, the relevant restrictions are scheduled to take effect on November 12, 2026. Eligibility will no longer depend solely on a user’s access location. It may also consider the company’s place of registration, headquarters, ownership structure, and actual control relationships.

This means that a company registered in a country or region where the service is permitted may not automatically be entitled to continue using it. If most of its ownership or actual control originates from a restricted jurisdiction, its employees, development teams, and applications that access models through corporate accounts could all be affected. Companies involved in cross-border operations, outsourced research and development, group-wide management, or cloud-based deployment should assess these changes in advance.

1. Restrictions Are Expanding from Individual Users to Corporate Relationships

Traditional regional restrictions usually rely on a user’s network location, account location, or billing address. This approach is relatively straightforward, but it does not adequately cover enterprise use cases. For example, employees may work in a supported region, and the corporate account may be registered by a local entity, while the actual business is controlled centrally by a parent company in another region. In such cases, checking only the user’s location does not accurately reflect the compliance relationship that the service provider is concerned about.

The newer approach may generally cover the following situations:

First, an individual user is located in an unsupported country or region. Even if the user’s employer is registered in a supported region, that individual may be unable to access or call the service normally.

Second, the company is registered or headquartered in an unsupported country or region. In this situation, the company’s employees, internal systems, and related business accounts may be restricted as a whole.

Third, the company is registered in a supported region but is directly or indirectly owned, directed, or actually controlled by an individual or company from an unsupported region. In this scenario, the place of registration alone may not determine whether the company is eligible to use the service.

A small amount of foreign investment does not necessarily trigger a restriction. In general, the focus is on majority ownership, control rights, decision-making authority, or actual control, rather than excluding every company with any form of foreign shareholding. Companies should therefore assess shareholding percentages, voting arrangements, board control, contractual control, and group-level management relationships together.

2. The Scope Extends Beyond Browser-Based Access

If restrictions applied only to browser-based access, companies could potentially adjust their internal systems or developer integrations. However, AI services are now delivered through many channels. Policies may cover scenarios such as:

• Interactive use through a web browser;
• Model APIs for programmatic access;
• AI coding, code analysis, and office automation tools;
• Collaborative workspaces for teams;
• Model capabilities delivered through cloud computing platforms;
• Model services resold, packaged, or integrated by authorized distributors and service providers.

Companies should not assume that they are outside the scope of a policy simply because they have not directly created an official account. If an application calls a relevant model in the background, or if a business function is indirectly provided through a third-party integration, it may still be subject to the service provider’s regional compliance rules.

From a technical architecture perspective, companies should pay particular attention to model calls hidden inside business systems. Customer support platforms, code review systems, knowledge-base question-answering systems, and document summarization tools may not display the model provider’s name in the user interface, while relying on an external model API in the backend. If access to that API is revoked, the affected business functions could experience failed requests, timeouts, invalid quotas, or unexpected model changes.

3. Assessing the Reasonableness of These Policies Requires Multiple Perspectives

Whether these policies are reasonable cannot be determined only by asking whether they make enterprise use more convenient. Regulatory, technical, and commercial risks must also be considered.

From a service provider’s perspective, restricting access according to corporate control relationships has a reasonable basis. AI models may involve data security, export controls, intellectual property, privacy protection, and national security concerns. Assessing risk solely by access location can make it easier to bypass controls through proxy access, shared cross-border accounts, or transfers within a corporate group. Including the place of registration, headquarters, and control relationships can improve consistency in policy enforcement.

From a company’s perspective, these rules also create significant uncertainty. Many multinational enterprises have multi-layer ownership structures, and their ultimate owners, investment percentages, and board arrangements are not always easy to assess. An independently operated overseas research and development entity could fall within a restriction because of its group ownership structure. For companies that rely on AI for code generation, testing, document processing, or customer service, a sudden interruption could directly affect productivity.

From a fairness perspective, the reasonableness of the rules also depends on whether they are implemented transparently. Companies need clarity not only about whether access will be restricted, but also about the following questions:

1. Are the assessment criteria public and sufficiently clear?
2. How will indirect control and contractual control be determined?
3. How will minority interests, convertible rights, and special voting rights be calculated?
4. Can a company submit supporting materials for review?
5. How will existing contracts, paid accounts, and historical data be handled?
6. When services are accessed through a cloud platform or integrator, which party bears responsibility?

If a policy provides only broad restriction language without appeal, review, or transition procedures, companies will have difficulty making accurate compliance assessments. By contrast, clear definitions, documentation requirements, and review processes would make the policy easier to implement.

4. Companies Should Conduct Four Immediate Reviews

1. Create an Inventory of AI Services

Companies should identify every model-related service they use, including browser-based accounts, API keys, internal applications, automated tasks, developer tools, cloud-based model calls, and vendor-integrated capabilities. At a minimum, the inventory should record the service name, responsible department, calling location, data types, business criticality, and difficulty of replacement.

2. Map Legal Entities and Control Relationships

Companies should not review only business registration documents or places of incorporation. They should also verify ultimate beneficial ownership, direct and indirect ownership percentages, voting rights, board seats, group management agreements, and actual operational control. For multi-layer ownership structures, it is advisable to create a simple control-relationship diagram and retain supporting documentation.

3. Review Data Flows and Deployment Locations

Companies should determine whether model inputs contain source code, customer information, internal documents, personal information, or trade secrets. They should also identify which regions the data passes through and which entities process it. Even if the service remains available, cross-border data transfers may create separate risks if they do not comply with internal policies or applicable laws.

4. Assess Business Interruption Impact

For systems that depend heavily on models, companies should define emergency response levels for service unavailability. Critical production processes should have alternatives such as manual processing, rule engines, private deployments, or other compliant model services. These fallback procedures should be tested to verify that switching is practical.

5. Technical Architectures Should Avoid Single-Service Dependence

Companies should avoid binding critical business operations entirely to a single external model service. A more resilient approach is to add a model adapter layer at the application level, separating business logic from a specific model interface. The adapter layer can centrally handle request formats, authentication, timeout and retry behavior, logging, sensitive-data filtering, and model routing.

A representative flow is shown below:

Business system
   ↓
Model adapter layer
   ├─ Region and permission checks
   ├─ Sensitive-data redaction
   ├─ Request auditing and logging
   ├─ Primary model service
   └─ Backup model or human-processing workflow

This design does not bypass policy restrictions, but it can reduce the cost of architectural changes when service availability changes. Backup services must still be assessed for legal compliance, data security, and output quality. Companies should not switch blindly to an unreviewed service simply to maintain availability.

API keys should also follow the principle of least privilege. Multiple regions or teams should not share the same credentials unnecessarily. Companies can manage access separately by legal entity, business system, and deployment region, while implementing usage auditing, anomaly alerts, and automatic deactivation. This makes usage easier to trace and helps identify affected applications quickly when policies change.

6. Procurement and Contract Management Are Equally Important

Technical teams often focus on whether an API can be called, but a company’s actual risk may also arise from contracts and the supply chain. Procurement teams should carefully review service terms covering regional eligibility, changes in control, account suspension, data processing, log retention, and service termination.

If the company may undergo an ownership change, group restructuring, or migration of business operations to another entity, it should assess in advance whether those changes could affect service eligibility. For critical systems, contracts and internal procedures should include change-notification mechanisms so that the company does not discover the loss of model access only after an organizational change has been completed.

Companies should also avoid transferring all compliance responsibility to an integrator or cloud platform. Even when model capabilities are delivered through an intermediary, the end user still needs to understand the underlying model provider, data-processing path, and applicable regions. Vendors may provide technical access and operational support, but the company remains responsible for its business purposes, data content, and internal authorization controls.

7. How Should Companies View These New Rules?

Overall, using a company’s place of registration and actual control relationship as a basis for regional restrictions is not without justification, particularly when highly capable AI services are provided across borders. However, the ultimate reasonableness of the policy depends on whether the rules are clear, enforcement is consistent, review mechanisms are adequate, and companies receive a reasonable transition period.

For companies, the least effective approach is to wait until service access is suspended before attempting to migrate accounts or replace integrations. A more reliable strategy is to treat the policy change as an opportunity to improve AI dependency governance across the organization: clarify ownership and control relationships, inventory model usage, protect sensitive data, build replaceable technical architectures, and prepare downgrade and human fallback procedures for critical operations.

In the future, the availability of AI services will no longer be only a technical issue. It will also be closely connected to corporate structure, data governance, cross-border operations, and supply chain management. Technology leaders, legal teams, procurement departments, and information security teams should participate in the assessment together. Only by translating policy requirements into asset inventories, permission systems, contract terms, and incident response plans can companies maintain business continuity as the rules evolve.