What is the relationship between personal information law and AI API? Things Taiwanese companies must understand before importing

The relationship between personal information law and AI API is very direct: as long as a company sends personally identifiable information to an external AI API, it is essentially not just using a tool, but handing over the data to a third party for processing, so it will definitely fall into the judgment scope of personal information law, contractual liability and data governance.

This is why many AI projects are not stuck in technology, but in legal affairs, information security and internal review. OpenAI, Anthropic, and Google all have different rules for data use, retention, and sharing; GDPR also clearly requires that data processing must comply with principles such as legality, purpose limitation, and data minimization.

One of the most common misunderstandings when many companies import AI APIs is: "This is just using an AI tool to help, not data outsourcing." But as long as the data leaves the company's original control environment and is sent to the external supplier's platform for processing, the problem is no longer just whether the function is good or not, but how this behavior should be viewed legally, whether it can be done in contracts, and whether it can prove that it has control in terms of governance.

Let’s start with the conclusion: AI API is not inherently illegal, but it is certainly not a legal vacuum

The use of AI API by enterprises does not necessarily mean it is illegal; the real problem is usually not the “use of AI” itself, but the failure to first clarify the legal logic of data processing.

OpenAI clearly states that API and commercial data will not be used to train models by default; Anthropic also uses default training for commercial products and APIs; Google Gemini API says that for projects with billing enabled, the default logs will be retained, and whether they are shared with Google for product improvement and model training depends on whether additional options are enabled. These differences remind enterprises that AI APIs are not homogeneous commodities, and legal risks are not automatically the same.

What companies should really think about first is not whether the model is strong or not

First, whether the data you send in is personal information, or whether it is enough to re-identify the individual.

Second, does this fall within the scope of the original purpose of collecting and using the data?

Third, are you acceptable to the supplier’s data retention, data sharing, training policies and cross-border processing conditions?

If these three questions are not answered clearly first, no matter how good the model is, legal affairs may still block the project.

Why does the Personal Information Law govern AI API? The key is not AI, but "third-party processing"

Many people think that personal information law mainly deals with data leaks, hackers or obviously illegal collection, but in fact, the core issue of personal information law is: have you processed the data within the legal, necessary and explainable scope?

The core principles listed in Article 5 of the GDPR are legality, fairness, transparency, purpose limitation, data minimization, correctness, storage limitations and integrity confidentiality. Although these principles are expressions of GDPR, they are very appropriate for understanding the risks faced by Taiwanese companies when using AI APIs, because they correspond to the things that companies most often ignore: why to send, how much to send, who to send, and how to manage it after sending.

Why "just taking a summary" doesn't mean it's okay

Because the law is looking at the data processing behavior, not what you subjectively think you are doing. When enterprises send data to external APIs, it does not automatically become a low-risk behavior just because the purpose is summary, analysis, classification, and organization.

As long as the data enters the third-party platform, you must re-examine:

Is there a legal basis for such use

Is it still within the original collection purpose

Is it beyond the necessary scope

Is there a basis for notification and management

This is why the phrase "AI API is just a tool" is usually untenable in the eyes of legal affairs.

What are the things that personal information law really cares about?

This article does not focus on the checklist, but breaks down the legal logic clearly. As long as enterprises understand the following core principles, they can understand why AI API will enter the scope of personal information law.

First, collection and use must have specific purposes

When companies originally collect customer information, they usually have a clear scenario, such as contract fulfillment, customer service, member management, after-sales processing, and marketing contact. The problem is that just because it can be used for customer service does not mean that it can be naturally extended to "use external AI API analysis." The purpose limitation principle of GDPR clearly requires that data utilization cannot deviate too far from the original purpose. This is also the most easily overlooked point when enterprises introduce AI.

What does this mean for AI API

It means that companies cannot just say "this is to improve services anyway." You need to be able to explain more specifically:

What is the purpose of sending data to the AI API this time

Why is this purpose reasonably linked to the original collection purpose

Why can't it be accomplished with less data

If you can't answer, the legal risk will increase.

Second, the information cannot exceed the necessary scope

This is the most commonly overlooked point when many companies import AI APIs. The data minimization principle of GDPR requires that data be adequate, relevant and limited to what is necessary. Returning to the situation of enterprise AI API, this is: instead of sending the entire package if you have the data, you can only send the part that is really needed to complete the task.

This is also directly related to AI Token

Because if a company does not minimize data first, not only the legal risks will increase, but the cost of AI Token will also increase. If you send in overly long customer service records, complete CRM fields, entire meeting records or a whole package of knowledge documents, the problem is not just compliance, but:

Risks are harder to prove controllable

So data minimization is not only a legal concept, but also a cost management concept when enterprises introduce AI APIs.

Third, be sure to read the third-party processing and retention rules

OpenAI’s commercial information page states that APIs and commercial data are not used for training models by default and provide data retention controls; Anthropic says that commercial products and APIs are not used for training by default, and Anthropic can be used as a processor of customer data in commercial scenarios; Google Gemini API clearly states that logs, datasets, feedback and data sharing are settings at different levels. Taken together, these things actually answer a core question: how will the data you send out be stored, processed, and controlled in the future.

Why can't companies just look at "no training"

Because "no training" does not mean "no data retention or recording". What really needs to be looked at include:

Whether logs will be retained by default

Whether retention can be set

Under what circumstances will the purpose be changed due to feedback or dataset sharing

These are not trivial matters, but core issues that the corporate legal affairs and information security committees are directly concerned about.

Fourth, cross-border processing is not an incidental issue, but one of the main issues

As long as companies send data to overseas supplier platforms, it will be difficult not to encounter cross-border issues. The most important thing here is not to memorize the law, but to understand the logic: once the data leaves the control boundaries of the original organization and region, legal affairs will definitely ask where the data is, who retains it, and which system it is subject to.

OpenAI’s corporate profile page mentions that it can provide retention control; Google bundles Gemini API logs and project management under the developer platform logic; Anthropic also separately explains the data usage and role relationships between commercial products and APIs. All these show that cross-border is not an abstract issue, but a practical issue that companies must ask when purchasing.

Why is the legal department particularly stuck on the AI API project?

Because from the perspective of legal affairs, the AI API project is not a simple technical upgrade, but a change in the data processing path. Data that was originally processed within the company will now be sent to external APIs; what was originally just customer service data may now become AI-assisted analysis data; what was originally just an internal tool may now become a system that can be accessed across teams and regions.

These changes will naturally make the legal department ask:

Are the supplier terms acceptable

How to divide the liability in the event of an accident

Can the company prove that it has controls?||So the legal department is not opposing AI, but confirming whether the company has fully thought about "third-party processing".

The relationship between personal information law and AI API is essentially about "the company handing over data to a third party for processing". Will it fall within the scope of legality, necessity, explanation and controllability? What we really need to understand is not how powerful AI is, but why the data can be sent, how much can be sent, how the supplier handles it after it is sent, and whether the company itself can prove that it has governance. As long as this logic is not established first, problems can easily arise later in import, procurement, legal review, or customer communication.

Will you definitely encounter personal data laws when using AI API?

As long as the information you send in is personal information or information that is sufficient to identify an individual, it will definitely fall within the scope of judgment under the Personal Information Law or similar data protection regulations. This is not a special case of AI, but an inherent problem with third-party data processing.

Does the Personal Information Law govern the model itself?

No. To be more precise, the Personal Information Law governs data processing behavior, including the purpose of collection, scope of use, whether it is necessary, whether it is safe, and whether there is a third party to process it. AI APIs only make these issues more apparent.

Does internal use also need to be controlled?

Yes. As long as you are processing data through an external AI API, it is not purely internal closed processing logic. Legal affairs and information security still need to intervene in judgment.

Why do legal professionals often say that you cannot directly throw customer information into AI?

Because this is not just a technical issue, but a comprehensive issue of data processing purposes, third-party provision, contractual obligations and supplier terms.

Is the Enterprise Edition API necessarily compliant?

企業版 API 就一定合規嗎？

uncertain. The enterprise version usually provides better data control and governance capabilities, but compliance depends on your usage methods, supplier terms, contractual constraints, and whether internal governance is in place.

Data source and credibility statement

This article is compiled and written in accordance with Taiwan's Personal Data Protection Act, GDPR and the official data use and retention policies of OpenAI, Anthropic and Google. It mainly refers to the following sources:

OpenAI｜Business data privacy, security, and compliance

Anthropic｜Does Anthropic act as a Data Processor or Controller?

Anthropic｜Is my data used for model training?

Google Gemini API｜Data Logging and Sharing

The content is organized in a three-layered manner of "data protection principles × third-party processing logic × enterprise import context". The purpose is to help enterprises see the relationship between personal information law and AI API as an understandable legal logic issue, rather than just an abstract compliance slogan.

This article belongs to the category "Enterprise AI Import and Data Security".

This category mainly organizes the data governance, legal terms, procurement risks, Taiwanese corporate practical issues and internal data boundaries that companies most often encounter before introducing AI APIs, AI tools and model platforms. It helps legal, information, procurement and management use the same language to assess risks, instead of waiting until they go online to fix loopholes.

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What is the relationship between personal information law and AI API? Things Taiwanese companies must understand before importing