Will Taiwanese companies be legally responsible for using AI APIs? Compilation of the risks most often ignored by enterprises

When Taiwanese companies use AI APIs, the answer is not "there is no responsibility at all", nor is "using it must be illegal", but once AI is integrated into the enterprise process, legal liability usually does not disappear, but only changes from manual work to the redistribution of data, processes, output and decision-making responsibilities.

Especially under Taiwan’s current legal system, what companies really need to look at first is often not “whether AI can be used”, but what legal boundaries they will encounter after using it: personal information, business secrets, copyrights, advertising and consumer protection, contractual responsibilities, internal control systems, and the governance direction brought by the “Artificial Intelligence Basic Law” announced in 2026.

Judging from the official documents of mainstream vendors, OpenAI clearly states that the data of the API platform and enterprise products will not be used to train models by default; Anthropic also stated that commercial product data will not be used to train generative models; Google differentiates between plans. The Gemini Developer API free tier data can be used to improve products, but the paid tier will not. Vertex AI also states that it will not use customer data to train or fine-tune models without permission or instructions. These clauses can reduce some risks, but they cannot directly transfer the entire legal responsibility to the supplier, because how the company collects, processes, inputs, authorizes, reviews and uses the output is still the core of the responsibility.

Why Taiwanese companies’ legal liability will not disappear automatically if they use AI API

The first mistake that companies often make when importing AI APIs is to treat it as an "external tool" instead of "part of the enterprise process." But from a legal perspective, as long as AI participates in the company's data processing, content production, customer service response, internal judgment or external messaging, responsibility will not be interrupted because of an additional layer of API.

AI is a tool, not a liability cutter

If a company uses AI API to write customer service replies, generate advertising copy, analyze customer information, summarize contracts, and generate bid content, and finally makes external errors, infringements, misleads, leaks, or violations of personal information rules, the outside world usually first looks at what the company has done, allowed, and controlled, rather than which model it is.

This is not because the supplier has no responsibility at all, but because the enterprise, as the party responsible for data control, content release and decision-making execution, still bears the main responsibility. This is a general principle of corporate legal compliance and internal control, and is also consistent with the principles of privacy and data governance, transparency, information security and responsibility emphasized in Taiwan's Artificial Intelligence Basic Law.

Taiwan's AI governance direction has been very clear

Taiwan's "Artificial Intelligence Basic Law" was announced on January 14, 2026. The competent authority is the National Science Council, and lists privacy protection and data governance, information security and security, transparency and explainability, fairness and non-discrimination, and accountability as core principles. This law does not directly tell companies "what to do if they use AI APIs," but it clearly sets a direction: AI is not an illegal zone in Taiwan, and companies will only need to clearly define risk classification, data governance, and internal responsibilities in the future.

The most common first-level legal risk: personal data

As long as Taiwanese companies send identifiable information of employees, customers, members, patients, students, applicants or partners into the AI API, they will easily encounter the "Personal Data Protection Act" first.

Personal information law governs not only leakage, but also collection, processing, and utilization

Taiwan's "Personal Information Protection Act" itself regulates the collection, processing, and utilization of personal information. The definition of personal information covers name, date of birth, ID number, contact information, financial status, social activities, medical treatment, genes, health examinations, and other information that can directly or indirectly identify a natural person. In other words, the question is not just "will it be leaked?" but whether the company has a legitimate basis to send this data into the modeling process.

After the law is revised in 2025, reporting and audit risks are higher

The Ministry of Justice’s official explanation of the review of international human rights conventions mentioned that after the personal information law is revised and promulgated in November 2025, government agencies are expressly required to notify the parties of personal information leakage and other incidents, and report to the competent authorities when conditions are met, and the competent authorities may further initiate audits and inspections. Although this paragraph mainly describes the public sector, it also clearly reflects that Taiwan’s supervision of personal information incidents is strengthening. For enterprises, this means that as long as AI APIs involve personal data, internal records, classification and incident response mechanisms are indispensable.

What is really prone to problems is not the model, but the process

Many companies do not intentionally break the law, but directly paste customer lists, customer service records, meeting transcripts or employee information into the test prompt during the trial phase. This kind of risk is often not something that can be solved by supplier terms alone, but rather whether the company has implemented data minimization, de-identification, authority control and internal prohibition rules. Both OpenAI and Google have abuse monitoring, short-term retention or logging mechanisms, so even if the supplier does not use data to train the model by default, it does not mean that retention, logging and permission issues can be ignored.

The second level of risk: business secrets and confidential information

In many companies, the first thing the AI API receives is not public information, but internal SOPs, product specifications, quotations, customer needs, bidding documents, supply chain information and meeting minutes. The most direct legal issue at this time is often not personal information, but confidential leakage and business secrets.

Trade secrets are not a risk only if they are stolen

The core of a business secret usually lies in: the information is not easily accessible to ordinary people, it has economic value, and the right holder has taken reasonable confidentiality measures. If an enterprise sends a large amount of originally protected data into external AI workflows without clear control, the risk may not necessarily turn into litigation immediately, but it can easily weaken the original confidentiality management foundation first. Even if the full text of the law is not quoted in this part, the risk assessment is clear: if a company does not draw a line first, it is most likely to lose in internal control rather than in technology.

What is most easily overlooked is the department’s own trial.

Many legal, business, marketing, purchasing, and customer service departments will first use personal accounts or free tools to test. At this time, what is most commonly used in the model is the first-hand content closest to commercial secrets. What enterprises really need to guard against is not just external hackers, but "internal employees sending out content they shouldn't."

The third layer of risk: copyright and content source responsibility

When companies use AI APIs to generate content, another common risk is copyright.

Not only the output will cause infringement, but the input may also have problems

If the company sends other people's protected articles, reports, internal teaching materials, competitive product content, and documents with unknown authorization into the model in a package, and then requires summary, rewriting, reorganization, or reproduction, the legal problem does not necessarily lie in the model itself, but may first arise in whether you have the right to use the original content in this way. This is also a point that many companies tend to overlook when importing AI: it is not only the AI-generated results that need to be looked at, but also the original materials.

The risks really begin after the generated content is released to the outside world

If the marketing copy, white papers, product pages, briefings, and customer service knowledge documents generated by the AI API are officially used by the company, then whether the content misuses other people's works, whether it is too close to existing expressions, and whether it contains unauthorized materials will become the company's own risks, and it cannot be cut off by saying "it was written by AI".

The fourth level of risk: advertising, consumer protection and external representation responsibilities

If Taiwanese companies use AI APIs to generate external customer service responses, product descriptions, promotional copy, comparative content, efficacy explanations or investment-related statements, the risks they are most likely to encounter are consumer protection and false representation risks.

If AI is wrong, it does not mean that the company has no responsibility

If the content generated by AI is directly used by the company to put it on shelves, send letters, place advertisements, or reply to customers, then what the outside world sees in the end is company information, not model experiments. The risk most often ignored by enterprises is to turn "draft tools" directly into "formal output tools" without an audit layer in between.

Wrong information may turn into liability for consumers, customers and partners

Product specifications, price conditions, scope of application, return and exchange commitments, effect descriptions, delivery dates and warranties, etc., once generated by AI and not reviewed, may end up not being simply wrong answers, but turning into contract disputes, consumer disputes or even advertising disputes. The focus of this type of risk is not on the AI technology, but on whether the company treats the AI output as an "unreviewed draft" rather than a "formal expression that can be directly communicated to the outside world."

The fifth layer of risk: Contractual responsibilities and supplier management

As soon as an enterprise starts to use AI API, it usually not only has internal problems, but also enters into supplier terms and contractual responsibilities.

The same supplier may have different rules for different product lines

OpenAI, Anthropic, and Google do not have "all products have the same set of data rules." For example, Google's Gemini Developer API free tier data can be used to improve the product, but the paid tier cannot; Vertex AI is another enterprise cloud route. This means that if the company does not first clarify which solution is being used, the risk may be that it goes wrong from the beginning.

If you misread the terms, the internal data policy may be completely misaligned

The real common problem is not that the supplier conceals it, but that no one within the company has fully read the terms, reservations, regions, caches, logs, and information security documents. In this case, technically it looks like AI is being imported, but legally it may be that supplier review has not been completed.

The sixth level of risk: employees use it directly, but the company has no system

Many companies have not officially launched AI API, but the risk has already occurred because employees have already used it at work.

No system is more dangerous than having tools

The real high-risk situation is usually not that the company has officially purchased the API, but that the company has no rules at all, causing everyone to work with different accounts, different tools, different information, and different prompts. In this case, companies not only cannot see the flow of data, but also find it difficult to audit who has sent what content.

The first thing companies should do is internal use policies

Before formally talking about models, RAGs, and workflows, companies should actually write down clearly:

Which data cannot be entered into the model, which scenarios must be anonymized, which outputs cannot be directly exposed to the outside world, which departments must first undergo legal or security review, which plans or account types cannot be used to access internal data

These types of rules are not necessarily complicated, but they must be in place first. Otherwise, it is not that responsibility does not exist, but that it is so scattered that no one can be found to take responsibility.

What is most easily overlooked by Taiwanese companies is not the laws, but the boundaries

Many companies’ understanding of legal risks still stops at “will AI break the law?” But what really happens more often is not the model itself, but the fact that these boundaries are not drawn first:

Data boundaries: which data can be entered and which cannot be entered

Personnel boundaries: who can use it, who can approve it, and who can see the results

Product boundaries: free tier, paid tier, Is the enterprise version and cloud version mixed?||Usage boundaries: Which ones can only be used as auxiliary and which ones cannot be directly exposed to the outside world

Audit boundaries: Which outputs must be manually reviewed

If these things are not done first, legal liability will easily surface all at once when something goes wrong.

Taiwanese companies using AI API may of course have legal liability. The real question has never been "Will using an AI API automatically break the law?" but rather whether the company treats AI as a managed enterprise process rather than an external tool that anyone can mess with.

Under Taiwan’s 2026 legal and policy direction, if companies integrate AI into internal data and daily operations, the first thing they should look at is not the model rankings, but personal information, confidentiality, copyright, external statements, supplier terms, log retention, region and internal control rules. Taiwan's "Artificial Intelligence Basic Law" has listed privacy, data governance, information security, transparency and responsibility as governance directions; official documents from suppliers have also written training, retention, regional and commercial data processing rules more clearly than before. This means that what companies really need to do is not to avoid using AI, but to draw clear boundaries first, and then decide how to go online safely.

Is it illegal for a Taiwanese company to use AI API?

No. The legal risk usually lies not in "whether it is useful", but in how it is used, what information is used, in which processes it is used, and whether there are appropriate controls.

What type of legal problems do you usually encounter first?

The most common ones encountered first are personal information, confidentiality and external content responsibility. Especially when companies send internal data, customer data or unaudited content directly into the model or directly use it externally, risks will emerge the fastest.

If the API provider says it doesn’t use data for training, is it safe?

does not mean completely safe. Training is only one part, it also depends on retention, logs, regions, permissions, cache and internal process management.

What should you do first before importing an enterprise?

Usually, instead of selecting a model first, we do data grading, scene inventory, supplier terms review and internal usage rules.

Is there a big difference between the free tier and the paid tier?

It may be much different. At least on Google's Gemini Developer API, there are clear differences between the free and paid tiers in terms of whether data can be used to improve the product.

What is the difference between this article and general AI API teaching articles?

This article is not to teach you how to apply for an API, nor is it to talk about platform differences in general, but to focus on the pre-implementation issue of "How will legal liability arise after Taiwanese companies use AI APIs?"

If you want to first understand whether the internal data of the company can be connected to the AI API, you can also go back to the internal data of the company. Can the AI API be used? Before importing, first understand the risks and boundaries, and first clarify the data usage boundaries and import risks.

If you want to understand the model, API, platform and usage from a more complete perspective, you can also go back to the AI Token summary page and take a look.

Data source and credibility statement

This article focuses on the legal and compliance risks of Taiwanese companies importing AI APIs. It mainly refers to Taiwan's official regulations and official supplier documents, including Taiwan's "Artificial Intelligence Basic Law" announced in 2026, Taiwan's "Personal Data Protection Law", OpenAI's Business Data Privacy and Enterprise Privacy, Google Cloud's Vertex AI and zero data retention and Gemini API Pricing.

The focus of this article is not to provide legal advice for individual cases, but to help companies understand the most common sources of liability: the legality before the data enters the model, the governance after the data enters the model, and the corporate responsibility after the output is officially used.

Taiwan's "Artificial Intelligence Basic Law" was promulgated on January 14, 2026, and listed privacy protection and data governance, information security, security, transparency and accountability as governance principles; OpenAI officially stated that the data of the API platform and enterprise products will not be used to train models; Google Cloud also made it clear that on Vertex AI, customer data will not be used to train or fine-tune AI/ML models without the customer's prior permission or instruction.

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

This category focuses on data security, governance, permissions, boundaries and import risks that are most easily overlooked before enterprises integrate AI into internal processes. It is suitable for readers who no longer just want to know whether AI is easy to use, but start to think about whether the data can be accessed, how to access it, and how to control it after accessing it.

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Will Taiwanese companies be legally responsible for using AI APIs? Compilation of the risks most often ignored by enterprises