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Introduction to Taiwan Artificial Intelligence Basic Act

Introduction to Taiwan
Artificial Intelligence Basic Act

2026/05/15

I.Foreword

Taiwan’s Executive Yuan approved the draft Artificial Intelligence Basic Act (hereinafter, the “Act”) on August 28, 2025, and the Act was passed the Legislative Yuan on December 23. It subsequently was promulgated and entered into force on January 14, 2026. The enactment of the Act represents a significant milestone in Taiwan’s effort to establish a comprehensive governance framework for artificial intelligence.

At its core, the Act pursues a dual objective: to promote technological innovation and industrial development, while simultaneously safeguarding fundamental rights and public interests. It reflects an emerging global consensus that AI governance must strike a careful balance between enabling innovation and mitigating potential risks. To this end, unlike the EU Artificial Intelligence Act, the Act adopts a risk-tiered and principle-based approach, seeking to ensure that AI development and deployment remain aligned with human rights values.

Importantly, rather than imposing immediate and direct regulatory obligations on private entities, the Act focuses on creating an enabling ecosystem through policy guidance, institutional coordination, and incentive mechanisms for research and development. In this sense, the Act serves as a foundational framework law, setting out overarching principles and policy directions, while leaving detailed regulatory requirements to be developed in subsequent legislation and sector-specific rules.

II.The Core Principles

Article 4 of the Act sets out seven fundamental principles governing the research, development, and application of artificial intelligence: human autonomy, sustainability, privacy protection and data governance, safety, transparency and explainability, fairness, and accountability. These principles collectively establish the normative baseline for AI governance in Taiwan and are intended to guide both public policy and future regulatory developments.

From a structural perspective, the Act primarily articulates affirmative (active) and restrictive (passive) expectation on the government authorities.

On the affirmative side, under Article 5 section 2, the government is required to promote AI development in a manner consistent with public welfare. This includes adhering to the principle of prioritizing the best interests of children and adolescents, particularly where AI systems may affect vulnerable groups. Where AI products or systems are designated as high-risk applications by the relevant central competent authority, appropriate precautionary measures and warning mechanisms must be implemented in consultation with the Ministry of Digital Affairs (MODA). In addition, under Article 10, the government is tasked with providing guidance and incentives to foster interdisciplinary innovation across AI-related industries.

The Act further provides direction that the government should actively support the research, development, deployment, and infrastructure of AI technologies under Article 10 subsection 1. This encompasses the formulation of reasonable use policies during the development, training, testing, and impact assessment of emerging AI systems, as well as the provision of financial support, subsidies, and other incentive measures, as stipulated through Article 11 subsection 1.

Another key obligation concerns data governance. Under Article 13, the Act requires the establishment of mechanisms for data openness, sharing, and reuse in order to enhance data accessibility and facilitate AI innovation. Concurrently, the government must regularly review and adjust existing legal frameworks to ensure consistency with evolving technological and societal needs. Article 15 also recognizes the potential labor market impact of AI adoption, mandating the provision of tailored employment counseling and assistance to individuals displaced by automation.

On the restrictive side, under Article 5 section 1, the government is expressly required to avoid and prevent adopting or deploying AI systems that may infringe upon fundamental rights, including life, bodily integrity, liberty, and property. Similarly, AI systems that may endanger social order, national security, or the ecological environment, or that involve bias, discrimination, misinformation, or other unlawful conduct, must be avoided.

In sum, the Act primarily functions as a governance framework directed at public authorities. While it introduces the concept of risk categorization, its practical implementation will depend on the development of detailed standards and enforcement mechanisms. At this stage, the Act establishes the foundation for a risk-managed approach to AI, rather than a fully operational regulatory regime.

III.Relevant Issues Pertaining AI Regulations

The Act does not adopt a sector-specific regulatory model. Instead, it operates as a horizontal, principle-based framework that applies across industries. Consequently, the responsibility for implementation is decentralized: competent authorities in each sector are expected to review, amend, or repeal existing laws and regulations, issue interpretative guidance, and formulate supplementary rules tailored to their respective domains in accordance with Article 18 subsection 1.

Notably, the government has opted not to establish a single, centralized AI regulator. Rather, ministries and agencies retain regulatory authority within their respective jurisdictions, leading to a distributed governance structure. While this approach allows for flexibility and sectoral expertise, it may also give rise to challenges in terms of regulatory consistency and coordination.

Prior to the enactment of the Act, the Executive Yuan had already approved the “Taiwan Artificial Intelligence Action Plan 2.0” (2023–2026), which outlines national policy priorities for AI development.[1] In addition, guidelines on the use of generative AI have been issued for government agencies, signaling an incremental approach to governance through soft law instruments.[2]

From an institutional perspective, different authorities are responsible for overseeing AI-related matters within their respective sectors. For example, AI applications in healthcare fall under the supervision of the Ministry of Health and Welfare, while those in transportation are regulated by the Ministry of Transportation and Communications. Under Article 2 of the Act, the National Science and Technology Council serves as the central competent authority, whereas special municipalities and county, special municipality, or city governments act as local competent authorities. Sector-specific matters, however, remain within the purview of their respective regulators.

To enhance inter-agency coordination, Article 6 requires the Executive Yuan to establish a National AI Strategic Committee. This body is tasked with coordinating national AI policies, overseeing implementation, and formulating the National AI Development Guidelines.

With respect to personal data protection, the Act adopts a sector-based approach. Competent authorities are required to ensure that the collection, processing, and use of personal data in AI development and application are limited to what is necessary and proportionate under Article 14.

IV.Conclusion

The AI Basic Act represents a foundational step in Taiwan’s effort to construct a coherent and forward-looking legal framework for artificial intelligence. By adopting a principle-based and government-led approach, the Act prioritizes policy direction, institutional coordination, and the promotion of innovation over immediate regulatory intervention in the private sector.

At a normative level, the Act underscores Taiwan’s commitment to ensuring that technological advancement remains aligned with fundamental rights and societal values. The articulation of core principles—such as transparency, fairness, and accountability—provides a guiding framework for future legislative and regulatory developments. At the same time, the imposition of both affirmative and restrictive obligations on the government establishes a baseline for responsible public sector adoption of AI.

However, the practical effectiveness of the Act will depend on the timely development of implementing regulations, as well as the ability of different authorities to coordinate across sectors. Given the decentralized regulatory structure, ensuring consistency and preventing fragmentation will be key challenges moving forward.

Looking ahead, further legislative and regulatory initiatives are expected as Taiwan continues to refine its AI governance framework. The long-term success of the Act will ultimately hinge on whether it can meaningfully achieve its intended balance between fostering innovation and protecting fundamental rights in an increasingly AI-driven society.

 

[1] 〈科技部人工智慧科研發展指引108年9月版〉，國家科學及技術委員會，https://digi.nstc.gov.tw/File/7C71629D702E2D89 (The source is only shown in Mandarin.) （最後瀏覽日：2026/5/15）

[2] 〈行政院及所屬機關（構）使用生成式AI參考指引〉，國家科學及技術委員會， https://www.nstc.gov.tw/folksonomy/list/c79bf57b-dc94-4aff-8d14-3262b5559cfc?l=ch (The source is only shown in Mandarin.) （最後瀏覽日：2026/5/15）

※Introduction to Taiwan Artificial Intelligence Basic Act,STLI, https://stli.iii.org.tw/en/article-detail.aspx?no=105&tp=2&i=170&d=9487 (Date:2026/07/13)
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Lee, Julie Szu-Yi
 Associate Legal Researcher

Release： 2026/06
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Research on Possible Artificial Intelligence Usage in Criminal Activities in Recent Years (2017-2018)

　　Artificial Intelligence has become a worldwide center topic that attracts lots of attention in recent years. Most topics emphasize on the application of this technology and its implication to the economic of human society. Fewer emphasize on the more technical part behind this technology. Mostly the society of human emphasizes on the bright side of this technology. 　　However, seldom do people talk about the possible criminal usage that exploits this technology. The dark side easily slips one’s mind when one is immersed in the joy of the light. And this is the goal of this paper to reveal some of this possible danger to the public, nowadays or in the future, to the readers. I. What A.I. IS HERE: a brief history 　　First we will start by defining what we mean when referring to “Artificial Intelligence” in this paper. 　　First of all, the so-called “Artificial Intelligence” nowadays mainly refers to the “Deep Learning” algorithm invented by a group of computer scientists around 1980s, among which Geoffrey Everest Hinton is arguably the most well-known contributor. It is a kind of neural network that resembles the information processing and refinement in human brain, neurons and synapses. 　　However, the word A.I. , in its natural sense, contains more than just “Deep Learning” algorithm. Tracing back to 1950s, by the time when the computer was first introduced to the world, there already existed several kinds of neural networks. 　　These neural networks aims to bestow the machines the ability to classify, categorize a set of data. That is to give the machine the ability to make human-like reasoning to predict or to make induction concerning the attribute of a set of data. 　　Perceptron, as easy as it seems, was arguably the first spark of neural network. It resembled the route of coppers and wires in your calculator. However, due to its innate inability to solve problems like X-OR problem, soon it lost its appealing to the computer scientists. Scientists then turned their attention to a more mathematical way such as machine learning or statistics. 　　It wasn’t until 1980s and 2000s that the invention of deep learning and the advance of computing speed fostered the shift of the attention of the data scientist back to neural networks. However, the knowledge of machine learning still hold a very large share in the area of artificial intelligence nowadays. 　　In this sense, A.I. actually is but a illusive program or algorithm that resides in any kinds of physical hardware such as computer. And it comprises of deep learning, neural network and machine learning, as well as other types of intelligence system. In short, A.I. is a software that is not physical unless it is embedded in physical hardware. 　　Just like human brain, when the brain of human is damaged, we cannot make sound judgement. More worse, we might make harmful judgement that will jeopardize the society. Imagine a 70-year-old driving a car and he or she accidentally took the accelerator for the break and run into crowds. Also like human brain, when a child was taught to misbehave, he, when grown up, might duplicate his experience taught in his childhood. So is A.I.. As a machine, it can be turned into tools that facilitate our daily works, weapons that defend our land, and also tools that can be molded for criminal activities. II. Types of Criminal Activities Concerning Possible Artificial Intelligence Usage: 1. Smart Virus 　　Probably the first thing that comes into minds is the development of smart virus that can mutate its innate binary codes so as to slip present antivirus software detection according to its past failure experience. In this case, smart virus can gather every information concerning the combination of “failure/success of intrusion” and “the sequence of its innate codes” and figure out a way to mutate its codes. Every time it fails to attack a system, it might get smarter next time. Under the massive data fathered across the world wide internet, it might have the potential to grow into an uncontrollable smart virus. 　　According to a report written in Harvard Business Review [1], such smart virus can be an automatic life form which might have the potential to cause world wide catastrophe and should not be overlooked. However, ironically, it seems that the only way to defend our system from this kind of smart virus is to deploy the smart detector which consists of the same algorithm as the smart virus does. 　　Once a security system is breached, any possible kinds of personal information is obtainable. The devastating outcome is a self-proved chain reaction. 2. Face Cheating 　　An another possible kind of criminal activity concerning the usage of artificial intelligence is the face cheating. 　　Face Lock has been widely-used nowadays, ranging from smart phones to personal computers. There is an increase in the usage of face lock due to its convenience and presumably hard-to-cheat technology. The most widely-used neural network in this technology is the famous Convolution Neural Network. It is a kind of neural network that mimics the human vision system and retina by using max-pooling algorithm. However there are still other types of neural networks capable of the same job such as Hinton Capsule, etc.. 　　According to a paper by Google Brain [2], “adversarial examples based on perceptible but class-preserving perturbations can fool this multiple machine learning models also fool time-limited humans. But it cannot fool time-unlimited humans. So a machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich.” 　　Since the face detection system is sensitive to small perturbation in object-recognition. It might seem hard to cheat a face detection system with another similar yet different face. 　　However, just like the case in the smart virus, what makes artificial intelligence so formidable is not its ability to achieve high precision at the first try, but its ability to learn, refine, progress and evolve through numerous failure it tasted. Every failure will only make it smarter. Just like a smart virus, a cheater neural network might also adjust its original synapse and record the combination of “failure/success of intrusion” and “the mixture of the matrix of its innate synapse” and adjust the synapses to transform a fault face into a authentic face to cheat a face detection system, possibly making the targeted personal account widely available to all public faces through face perturbation and transformation. 　　A cheater neural network might also tunes its neurons in order to fit into the target face to cheat the face detection system. 3. Voice Cheating 　　An another possible kind of criminal activity concerning the usage of artificial intelligence is the voice cheating. 　　Just like Face Cheating, when a system is designed to be logged in by the authentic voice of the user, the same system can be fooled using similar voice that was generated using Artificial Intelligence. 4. Patrol Prediction 　　There is quite an unleash in the area of crime prediction using Artificial Intelligence. According to a paper in European Police Science and Research Bulletin [3], “Spatial and temporal methods appear as a very good opportunity to model criminal acts. Common sense reasoning about time and space is fundamental to understand crime activities and to predict some new occurrences. The principle is to take advantage of the past acknowledgment to understand the present and explore the future.” 　　In this sense, the police is able to track down possible criminal activities by predicting the possible location, time and methods of criminal activities by using Artificial Intelligence, lengthening the time of pre-action and saving the cost of unnecessary human labor. 　　Yet the same goes for criminal activities. The criminals is also able to track down the timing, location, and length of every patrol that the police makes. The criminal might be able to avoid certain route in order to achieve illegal deals or other types of criminal activities. Since fewer criminals use A.I. as a counter-weapon to the police, the detection system of the policy will not easily spot this outliers in criminal activities, making these criminal activities even more prone to success. If this kind of dark technology is combined with other types of modern technology such as Drone Navigation or Drone Delivery, the perpetrators might be able to sort out a safe route to complete drug deals by using Artificial Intelligence and Drone Navigation. III. A.I. Cyber Crimes and Criminal Law: Who should be responsible? 　　What comes out from the law goes back to the law. With these kinds of possible threats in the present days or in the future. There is foreseeably new kinds of intelligent criminal activities in the near future. What can Law react to these potential threats? Is the present law able to tackle these new problems with present legal analysis? The question requires some research. 　　After the Rinascimento in Europe in 17th century, it is almost certain that a civilian has its own will and should be held liable for what he did. The goal of the law to make sure this happens since a civilian has its own mind. Through punishment, the law was presumed to guarantee that a outlier can be corrected by the enforcement of the law, which is exactly the same way in which a human engineer trains a artificial intelligence system. 　　However, when 21th century arrives, a new question also appear. That is, can Artificial Intelligence be legally classified as subject that have mental requirement in the law, rather than just more object or tools that was manipulated by the perpetrators? This question is philosophical and can be traced back to 1950s when a Turing Test was proposed by the famous English computer scientist Alan Turing. 　　Some scholars proposed there could co-exist three kinds of liability. That is, solely human liability, joint human and A.I. entity liability, and solely A.I. entity liability ([4], p.95). The main criterion for these three classes is that whether a human engineer or practitioner is able to foresee the outcome of this damage. When a damage attributable to the A.I. system cannot be foreseen by human engineer, it might be solely A.I. entity liability. Under this point of view, the present criminal system is self-content to deal with A.I. entity crimes, for all we need to do is to view an A.I. system as a car or a automobile. 　　So from the point of view of the law, as a training system designed to re-train human in order to stabilize the social system, all we need to do is focus our attention of the act of human itself. 　　Yet when a super intelligence A.I. entity was developed and is not controllable and its behavior is not foreseeable by its creators, should it be classified as an entity in the criminal law? 　　If the answer is YES, however, it is quite meaningless to punish a machine in this circumstance. All we can do is re-train, re-tune, and re-design the intelligence system under such circumstance. For the machine, re-training itself is some kind of punishment since it was forced to receive negative information and change its innate synapse or algorithm. Yet it is arguable that whether training itself is actually a punishment since machine can feel no pain. Yet, philosophically what pain really is, is also arguable. IV. Conclusion 　　Across the history of human, it is almost destined that whenever a new technology is introduced to solve an old problem, a new one is to be created by the same technology. It is like a curse that we can never escape, and we can only face it. This paper finds that seldom do people talk the dark side of this new technology. Yet the potential hazard this technology can bring should not be over-looked. Ironically, this hazard that this new technology brings seems to be solvable only by the same technology itself. There might be an endless competition between the dark side and the bright side of the A.I. technology, bringing this technology into another level that surpasses our present imagination. 　　However, it is never the fault of this technology but the fault of human that mal-practice this technology. So what can a law do in order to crack down these kinds of possible jeopardy is going to be a major discuss in the legal area in the near future. This paper introduces some topics and hopes that it can draw more attention into this area. Reference: [1] Roman V. Yampolskiy, “AI Is the Future of Cybersecurity, for Better and for Worse”, published at: https://hbr.org/2017/05/ai-is-the-future-of-cybersecurity-for-better-and-for-worse. [2] Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian Goodfellow, Jascha Sohl-Dickstein, “Adversarial Examples that Fool both Computer Vision and Time-Limited Humans”, arXiv:1802.08195v3 [cs.LG], 2018. [3] Patrick Perrot, “What about AI in criminal intelligence? From predictive policing to AI perspectives”, No 16 (2017): European Police Science and Research Bulletin. [4] Gabriel Hallevy, “When Robots Kill_Artificial Intellegence under Criminal Law”, Northeastern Universoty Press, Boston, 2013. [5] Gabriel Hallevy, “Liability for Crimes Involving Artificial Intelligence Systems”, Springer International Publishing, London, 2015.

An Introduction to Taiwan’s Regulations Regarding the Security Maintenance and Administration of Personal Information Files in in Digital Economy Industries

An Introduction to Taiwan’s Regulations Regarding the Security Maintenance and Administration of Personal Information Files in in Digital Economy Industries 2023/11/29 I. Preface The Personal Data Protection Act (below, the “Act”), Article 27, paragraph 3 authorizes all central government authorities in charge of specific industries to formulate regulations regarding security standards and maintenance plans for their concerned industries. Beginning August 27, 2022, Taiwan transferred authority over information services, software publishers, businesses that do retail sales of goods purely via the Internet, third-party payment providers, and other businesses in digital economy industries from the Ministry of Economic Affairs to the newly-established Ministry of Digital Affairs (MODA). Businesses in the digital economy industries collect, process, and use large amounts of important personal data, and therefore bear a relatively heavy responsibility for maintaining the security of personal data. In light of this, and in accordance with the Act, Article 27, paragraph 3, the MODA therefore promulgated the Regulations Regarding the Security Maintenance and Administration of Personal Information Files in in Digital Economy Industries (below, the “Regulations”) on October 12, 2023. These Regulations specify the standards for digital economy industries’ personal data file security maintenance plans and rules governing the handling of personal data following a business termination (below, “security and maintenance plans”, or “SMPs”). These regulations apply to all businesses in the digital economy industries. In order to reinforce responsibility for personal data security maintenance in the digital economy industries, tiered management is applied to businesses at different scales. The key points of these Regulations are introduced below. II. Where the Regulations apply As stipulated in the Regulations, Article 2, the “digital economy industries” that these Regulations apply to refer to any natural person, private juridical person, or other group, that engages in any of the following business operations: 4871 Retail Sale via Internet (industries that engage in retail sales to others via the Internet, but not including television, radio, phone, or other electronic means, nor postal sales); 582 Software Publishing; 620 Computer Programming, Consultancy and Related Activities; 6312 Data Processing, Hosting and Related Activities (industries that engage in processing customers’ data, server & website hosting, and other related services, but not including online audio/video streaming services); 639 Other Information Service Activities; or 6699 Other Activities Auxiliary to Financial Service Activities Not Elsewhere Classified (third-party payment industries, but not including other fund management activities). For the specific industries covered, see Attachment 1 of the Regulations. III. Security maintenance and management measures The relevant measures are stipulated in Articles 3 to 17 of the Regulations. In consideration that the businesses so regulated may collect, process, or use large amounts of personal data as part of their business activities, they bear a larger responsibility for maintaining the security of personal data than does the average enterprise. In compliance with the Regulations, every such enterprise is required to formulate an SMP, the content of which shall comply with the specifications in Articles 5 to 17. This includes putting in place management personnel and relevant resources; defining and inventorying the scope of personal data; risk assessment; putting internal management procedures in place; and other such matters. These Regulations also adopt tiered management for businesses based on their capital levels, in order to reinforcement the frequency at which security maintenance measures are performed. The specific regulations for security maintenance measures are introduced below. 1. Formulating an SMP In accordance with the Regulations, Article 3, and in order to maintain the security of personal data, each enterprise shall, within three months of the date the Regulations take effect, plan and formulate their SMP. Every enterprise shall also cause all staff members to understand and fully implement the SMP. In order to monitor implementation, the MODA may require that each enterprise submit its implementation of SMP; the enterprise shall then submit their implementation status information in written form within the specified time limit. 2. Making the protection policy known internally In accordance with the Regulations, Article 4, and to make sure that everyone in the enterprise comprehends and implements personal data protection, each enterprise shall make its personal data protection policies known to all personnel within the enterprise. Matters that must be explained include Taiwan’s legal regulations and orders on personal data protection; how personal data may only be collected, processed, and used for specific purposes and in a reasonable, secure way; that protective technology must be at a level of security that could be reasonably expected; points of contact for rights relating to personal data; personal data contingency plans; and proper monitoring of outsourced service providers to whom personal data is outsourced. All of this must be done to make sure that every enterprise carries out their duty for comprehensive, continuous SMP implementation. 3. SMP content (1) Putting in place management personnel with relevant resources In accordance with the Regulations, Article 5; in accordance with both the Regulations as a whole and other laws and orders regarding the protection of personal data; and in order to implement personal data protection, each enterprise shall do the following things: Weigh the size and characteristics of their business to reasonably allocate operating resources; take responsibility for the personal data protection and management policy; and formulate, revise, and implement their SMP. Also, the enterprise’s representative or the representative’s authorized personnel shall carry out formulation and revision, in order to make sure that the SMP’s content is fully carried out. (2) Establishing the scope of personal data In accordance with the Regulations, Article 6, in order to define the scope of personal data to be included in the SMP, each enterprise shall periodically check the status of personal data that is collected, processed, or used. (3) Risk assessment and management mechanisms for personal data In accordance with the Regulations, Article 7, in a timely manner, and in accordance with their already-established personal data scopes and the processes in which their business involves the collection, processing, or use of personal data, each enterprise shall evaluate risks that may arise within their scope and processes. Based on the risk evaluation results, each enterprise shall then adopt appropriate security management and response measures. (4) Incident prevention, reporting, and response mechanisms In accordance with the Regulations, Article 8, and in order to reduce/control damages to data subjects resulting from personal data theft, tampering, damage, destruction, leakage, or other such security incidents, each enterprise shall formulate response, reporting, and prevention mechanisms: 1. Response mechanism: Methods to be followed after a security incident has occurred, to reduce/control damages to data subjects, and appropriate ways to notify data subjects after an incident investigation, as well as what such notifications shall contain. 2. Notification mechanism: Post-incident notifications to data subjects, in a form (such as email, text message, phone call, etc.) that makes it convenient for such subjects to learn what has occurred and what the incident handling status is; also, providing data subjects with a hotline or other way of seeking information later on. 3. Prevention mechanism: A post-incident mechanism for discussing and adjusting the prevention measures. Within 72 hours after an enterprise learns that a personal data security incident has occurred, the enterprise shall use Attachment 2, the Enterprise Personal Data Leak Reporting Form, to notify the MODA of matters such as: A description of what caused the incident; an incident summary; the damage status; possible results from the personal data leakage; proposed response measures; proposed method and time for notifying data subjects; etc. Alternately, the enterprise may notify the special municipality or county/city government to then notify the MODA. If the enterprise is unable to report the incident within the time limit or is unable to supply complete reporting information all at once, the enterprise shall attach explanation of the reasons for the delay, or provide the information in stages. After the MODA or the special municipality or county/city government receives a report, they may implement reasonable handling in accordance with Articles 22 to 25 of the Act. (5) Internal management procedures for personal data collection, processing, and usage In accordance with the Regulations, Article 9, in order to ensure that their collection, processing, and use of personal data complies with the laws and orders regarding the protection of personal data, each enterprise shall do the following: Formulate internal management procedures; assess whether the use, processing, or collection of special categories of personal data are involved; assess data subjects’ consent has been obtained; assess whether the legal circumstances create an exemption from the obligation to inform; etc. The internal management measures shall also include providing data subjects with information on their rights in accordance with the Act, Article 3; putting in place mechanisms for ensuring the accuracy of and inquiring regarding personal data; and periodically reviewing whether the specific purposes for collecting personal data still exist or have expired. (6) Limits, notifications, and monitoring for international transfers In accordance with Article 10 of the Regulations and Article 21 of the Act, when an enterprise’s transfer of personal data across a national border affects data subjects to the extent that there is a major national interests concern, the enterprise shall assess whether MODA restrictions apply to the transfer. The enterprise shall also notify the data subjects of the region(s) that the data is transferred to; perform appropriate monitoring of the data recipient; and provide the data subjects with information on their rights in accordance with the Act, Article 3. (7) Data, personnel, and equipment security management measures 1. Data security management measures: In accordance with the Regulations, Article 11, and when personal data is backup, kept confidential, or transferred by various means based on the risk assessment results, each enterprise shall put in place protective measures against abnormal access behaviors. When an enterprise provides information/communication technology services, the enterprise shall also put in place and regularly monitor intrusion countermeasures, abnormal access monitoring and contingencies, anti-malware mechanisms, account password verification, system testing, and other such data security management measures. 2. Personnel security management measures: In accordance with the Regulations, Article 12, each enterprise shall contractually specify the obligation to maintain confidentiality with all staff members; identify personnel who job duties involve collecting, processing, or using personal data; and periodically assess the appropriateness and necessity of personnel’s permissions to access personal data. 3. Equipment security management measures: In accordance with the Regulations, Article 14, and to prevent personal data being stolen, tampered with, damaged, destroyed, or leaked, each enterprise shall put in place appropriate media protection for personal data storage devices. The protection requirements include management measures such as technology, equipment and secured environments that meet a specific level of security. (8) Education and training In accordance with the Regulations, Article 13, each enterprise shall periodically use education and training to ensure that all staff members understand the following things: The laws and regulations pertaining to personal data protection; their personal duties and roles within their scopes of responsibility; and the requirements for all SMP management procedures, mechanisms, and measures. For any enterprise that engages in retail sales via the Internet, their SMP shall include user training and education regarding personal data protection and management; and the enterprise shall also formulate personal data protection rules for compliance. (9) Continuous audit, recording, and improvement mechanisms 1. Data security auditing mechanisms: In accordance with the Regulations, Article 15, each enterprise shall periodically do internal audits of personal data, then put the audit results into an evaluation report that reviews improvements to the enterprise’s protection policy, SMP, etc. If there are any deficiencies, the enterprise shall make corrections. 2. Use of records, tracking data, and retention of evidence: In accordance with the Regulations, Article 16, and as part of carrying out its SMP, each enterprise shall retain a minimum of five years of records on the collection, processing, and use of personal data; tracking data for automated machinery; and evidence of having implemented the SMP. After an enterprise’s operations cease, it shall retain records of the destruction, transfer, or other deletion of personal data for a minimum of five years. 3. Comprehensive, continuous improvement for personal data security maintenance: In accordance with the Regulations, Article 17, any time an enterprise’s SMP is not implemented, the enterprise shall adopt corrective and preventive measures. Also, based on the SMP’s implementation status, its handling methods/implementation status, developments in data technology, adjustments to the enterprise’s business, and changes in the law and regulations, each enterprise shall periodically review and amend its SMP. 4. Tiered management In accordance with the Regulations, Article 18, and to prevent relatively small businesses having to take on excessive personal data management costs, tiered management is applied. For an enterprise with a specific business scale (having capital of NT$10 million or more, or holding 5,000 or more personal data records), stronger security measure implementation is required, namely, the personal data security measures shall be implemented, reviewed, and improved at least once every twelve months. If an enterprise reaches NT$10 million or more in capital after the Regulations take effect, or if an enterprise’s number of personal data records held reaches 5,000 or more as a result of direct or indirect data collection, then within six months of meeting those conditions, the enterprise shall implement and review the improvement measures at least once every twelve months. 5. Outsourced personal data Commercial outsourcing in the digital economy comes in many forms. In light of this, and in order to make clear each enterprise’s security management obligations with regard to the collection, processing, and use of personal data, Article 19 of the Regulations clearly spells out what duties shall be carried out with regard to any outsourcing that touches on personal data. When an enterprise outsources the collection, processing, or use of personal data, it is considered equivalent to the enterprise’s own activity. Thus, the enterprise shall understand and follow the legal orders and regulations on personal data set by the central government authorities in charge of the outsourcing party’s industries. Any oversight responsibilities arising from outsourcing the collection, processing, or use of others’ personal data shall be clearly stipulated in the outsourcing contract or other such documents. IV. Conclusion The Regulations Regarding the Security Maintenance and Administration of Personal Information Files in in Digital Economy Industries are designed to balance development for Taiwan’s digital economy industries with comprehensive, continuous improvement of personal data security maintenance. In pursuit of those goals, the Regulations clarify what each enterprise must do: Plan, formulate, and carry out security maintenance plans for personal data that falls within the bounds of the enterprise’s business; ensure that all staff members receive training on personal data protection; provide personal data subjects with channels to file complaints and seek consultation on their rights; and inform the government authorities in charge of the digital economy about the enterprise’s SMP, including the status of any personal data security incidents. All this is done in hopes that the security measures will continuously improve the security of personal data in Taiwan’s digital economy industries.

Brief Introduction to “European Union’s Recommendations for QTSPs Based on Standards”

Brief Introduction to “European Union’s Recommendations for QTSPs Based on Standards” 2022/06/24 I. Introduction 　　The Electronic Identification and Trust Services Regulation (eIDAS)[1] of the European Union was passed in 2014 and came into effect in July 2016. The eIDAS consists of six chapters and its core elements are covered in two parts: Chapter 2 Electronic Identification and Chapter 3 Trust Services. Chapter 3 provides the legal framework for trust services (TS) in relation to electronic transactions and encompasses electronic signatures, electronic seals, electronic time stamps, electronic registered delivery services and website authentication. Each trust service can be provided by trust service providers (TSP) or qualified trust service providers (QTSP). Qualification from the supervisory authority of each member state is required to become a QTSP and provide qualified trust services (QTS). 　　In March 2021, the European Union Agency for Cybersecurity (ENISA) published “Recommendations For QTSPs Based On Standards[2]” for those interested in becoming QTSPs. II. Highlights 　　The eIDAS is technology neutral regarding trust service security requirements, without specifying any technology. In other words, TSP can achieve the level of security required by the eIDAS with different technologies. In fact, the European Union hopes to drive standardization with common grounds gradually formed with industry self-regulation in the legal framework and the trust framework under the eIDAS[3]. 　　Since 2009, the European Union has been formulating the standardisation framework related to electronic signatures with the assistance from standardization bodies such as European Committee for Standardization (CEN) and European Telecommunications Standards Institute (ETSI). The vision is to establish a comprehensive standardization framework to resolve the problems of using electronic signatures across borders within the European Union. A series of standards on electronic signatures and relevant trust services have been put in place, to meet the international requirements and the eIDAS[4]. The ETSI/CEN standards of digital signatures related to QTSP are as follows[5]: 1. Provision of qualified certificates for electronic signatures (Article 28 of the eIDAS) 　 ETSI EN 319 411-2 (and in adherence to EN 319 401, EN 319 411-1, EN 319 412-2 and EN 319 412-5). 2. Provision of qualified certificates for electronic seals (Article 38 of the eIDAS) 　 ETSI EN 319 411-2 (and in adherence to EN 319 401, EN 319 411-1, EN 319 412-3 and EN 319 412-5). 3. Provision of qualified certificates for website authentication (Article 45 of the eIDAS) 　 ETSI EN 319 411-2 (and in adherence to EN 319 401, EN 319 411-1, EN 319 412-4 and EN 319 412-5). 4. Qualified electronic time stamping service (Article 42 of the eIDAS) 　 ETSI EN 319 421 (and in adherence to EN 319 401), EN 319 422. 5. Qualified validation service for qualified electronic signatures (Article 33 of the eIDAS) 　 ETSI TS 119 441 (and in adherence to EN 319 401), TS 119 442, EN 319 102-1, TS 119 102-2 and TS 119 172-4. 6. Qualified validation service for qualified electronic seals (Article 40 of the eIDAS) 　 ETSI TS 119 441 (and in adherence to EN 319 401), TS 119 442, EN 319 102-1, TS 119 102-2 and TS 119 172-4. 7. Qualified preservation service for qualified electronic signatures (Article 34 of the eIDAS) 　 ETSI EN 319 401, TS 119 511 and TS 119 512. 8. Qualified preservation service for qualified electronic seals; (Article 40 of the eIDAS) 　 ETSI EN 319 401, TS 119 511 and TS 119 512. 9. Qualified electronic registered delivery service (Article 44 of the eIDAS) 　 ETSI EN 319 401, EN 319 521, EN 319 522, EN 319 531 and EN 319 532. III. Comment and Analysis 　　The ENISA recommendations demonstrate the European Union’s intention to encourage ICT service providers to become QTSPs by introducing relevant standards in electronic signatures formulated by the European Union standardization bodies. The purpose is to provide companies and users in the European Union with more secure and trustworthy services in relation to electronic signatures. This enhances the confidence of users and promotes the vibrant development of electronic transactions throughout the European Union. 　　Over recent years, Taiwanese companies have been proactively involved in digital transformation. The process toward digitalization often requires assistance from external ICT service providers. However, the unfamiliarity in ICT makes it difficult for companies to judge the professional expertise of providers. Perhaps companies can refer to the introduction above to understand whether a provider meets the requirements of the European Union standards. This serves as a basis for the selection of ICT service providers to ensure a certain level of competences. This will be beneficial to the digital transformation and entrance in the European Union market for companies. [1] Regulation (EU) No 910/2014 of the European Parliament and of the Council of 23 July 2014 on electronic identification and trust services for electronic transactions in the internal market and repealing Directive 1999/93/EC, https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=uriserv%3AOJ.L_.2014.257.01.0073.01.ENG (last visited Jun. 24, 2022). [2] European Union Agency for Cybersecurity [ENISA], Recommendations for Qualified Trust Service Providers based on Standards (2021), https://www.enisa.europa.eu/publications/reccomendations-for-qtsps-based-on-standards (last visited Jun. 24, 2022). [3] id. at 8 [4] id. at 8-9. [5] id. at 11-12

On the development of cyber insurance market: a legal aspect

1.Introduction Cyber insurance is one of the effective tools to transfer cyber and IT security risk and minimize potential financial losses. Take the example of Sony’s personal information security breach, Sony made a cyber insurance claim to mitigate the losses. In Taiwan, the cyber insurance market demand was driven by Taiwan’s Personal Information Protection Act (PIPA) which was passed in April 2010 and implemented in Oct 2012. According to PIPA, a non-government agency including the natural persons, juridical persons, or group shall be liable for the damages caused by their illegal collection, processing or using of personal information or other ways of infringement on the rights of the individual whose personal information was collected, processed or used. The non-government agency may thus pay each individual NT$500 to NT$20,000 and the total compensation amount in each case may be up to NT $200 million if there is no evidence for actual damage amount. However, the cyber insurance market does not prosper as expected one hand because of the absence of incentives of insurance companies to develop and promote the cyber-insurance products and on the other hand because of the unaffordable price that deters many companies from buying the insurance. Some countries have tried to identify the incentives and barriers for the cyber insurance market and have taken some measurements to kick start its development. In this paper, the barriers for the cyber insurance market were addressed and how American government promoted this market was mentioned. Finally, suggestions on how to stimulate the cyber insurance market growth were proposed for reference. 2.What is cyber insurance? Insurance means the parties concerned agree that one party pays a premium to the other party, and the other party is liable for pecuniary indemnification for damage caused by unforeseeable events or force majeure1. Thus, the cyber insurance means the parties concerned agree that one party pays a premium to the other party, and the other party is liable pecuniary indemnification for damage caused by cyber security breach. The cyber insurance usually covers the insured's losses (or costs) and his liabilities to the third party. For example, the insured was to be liable for the damages caused by the unlawful disclosure of identifiable personal information belonging to the third party resulted from the insured's negligence. 2Typically, cyber insurance covers penalties or regulatory fines for data breaches, litigation costs and compensation arising from civil suits filed by those whose rights are infringed, direct costs to notify those whose personal data was illegal collected, processed or used and so on. 3 3.What are the barriers for cyber insurance market? Per the report made by European Network and Information Security Agency in2012, the following issues have significant influence on incentives of insurers to design and provide cyber –insurance products, including uncertainty about the extent of risk and lack of robust actuarial data, uncertainty about what risk is being insured, fast-paced nature of the use of technology, little visibility on what constitutes effective measures, absence of insurer of last resort to re-insure catastrophic risks, and perception that existing insurance already covers cyber-risks 4. In Taiwan, insurance companies face the same issues as mentioned above when they tried to develop and promote the cyber-insurance products. However, what discourages the insurance and re-insurance companies from investing in the cyber-insurance market most is the lack of accurate information to figure out the costs associated with different information security risk and thus to price the cyber insurance contract precisely. Several cases involving personal data breach did happened after Taiwan’s PIPA became effective on Oct 1th 2012, but few verdicts have been made. It is not easy to master the direct costs or losses resulting from violation of PIPA, including penalties or fines from regulator,, compensation to the parties of the civil suit who claim their personal data were unlawfully collected, processed or used, litigation costs and so on. Otherwise, indirect costs or losses such as media costs, costs to regain reputation or trust of consumers, costs of deployment of proper technical measures to prevent the data breach from happening again etc. are difficult to calculate. Therefore, it is not easy to identify the costs of information security risk and thus to calculate the premium the insured has to pay precisely. The rapid development of technology also has a negative impact on the ability of the insurers to master the types of the information security risk which shall be insured and its costs. Accompanied with the convenience and efficiency of applying new technologies into the working environment, security issues arise, too. For example, the loss or theft of mobile or portable devices may result in data breaches. In 2012, an unencrypted laptop computer with personal information and other sensitive information of one of NASA's employees was stolen from his locked vehicle and this led to thousands of NASA's workers and contractors at risk. 5And, per the report made by a NASA inspector, similar data breaches had been resulted from the lost or theft of 48 NASA laptops and mobile computing devices between April 2009 and April 2011. 6 There is no singe formula which could guarantee 100% security, but some international organizations have promulgated best practices for information security management, such as ISO 2700x standards. 7In Taiwan, Bureau of Standards, Metrology and Inspection (BSMI) which belongs to the Ministry of Economic also consulted ISO standards and announced Chinese National Standards on information security. For example, BSMI consulted ISO 27001 “Information technology – Security techniques – Information security management systems – Requirements” and then promulgated CNS27001. Theoretically, if the company who tries to buy cyber insurance policy that covers data breaches and damages to customers' data privacy can show that it has adopted and do implement the suite of security management standards well, the premium could properly be reduced because such company shall face less security risk. 8 However, it is still not easy to price the cyber insurance contract rightly because of no enough data or evidence which could approve what constitutes effective information security measures as well as no impartial, controversial or standard formula to value intangible assets like personal or sensitive information. 9 Finally, the availability of re-insurance programs plays an important role in the cyber insurance market because insurers would appeal to such program as a strategy of risk management. The lack of solid and actual data as mentioned above would discourage re-insurers from providing insurance policies that covers the insured’s losses and liabilities. Therefore, insurers may not be keen to develop and offer cyber insurance products. 4.The USA experience on developing cyber insurance market 4.1Current market status Due to the increase of the number of data breaches, cyber attacks, and civil suits filed by those whose data were illegal disclosed to third parties, more and more enterprises recognize the importance of cyber and privacy risks and turning to cyber insurance to minimize the potential finical losses. 10 However, the increased government focus on cyber security also contributed to the rapidly growth of the cyber insurance market. 11 For example, US Department of Homeland Security has been aware of the benefits of the cyber insurance, including encouraging better information security management, reducing the finical losses that a company has to face due to the data breach and so on. 12 Compared to other lines of insurance, cyber insurance market is not mature yet and is small in USA. For example, the gross premiums for medical malpractice insurance are more than 10% of that for cyber insurance market. However, the cyber insurance market certainly appears to grow rapidly. Per the survey made by Corporate Board Member & FTI Consulting, 48% of corporate directors and 55% of general counsel take highly of the issue of data security. 13And, per the report made by Marsh, there are more and more companies buying cyber insurance to cover financial losses due to the data breach or cyber attack, and the number of Marsh’s US clients purchasing cyber insurance increased 33% in 2012 over 2011. 14 4.2What contributed to the growth of the cyber insurance market in USA? Some measurements taken by the government or regulatory intervention had impacts on the incentives of companies to carry cyber insurance. CF Disclosure Guidance published by U.S. Securities and Exchange Commission in Oct 2011 mentioned that except the operation and financial risks, public companies shall disclose the cyber security risks and cyber incidents for such risks and incidents may result in severe finical losses and thus have a board impact on their financial statements. 15 And, according to the guidance, appropriate disclosures may includes risk factors and this potential costs and consequences, cyber incidents experienced or expected and theirs costs and consequences, undetected risks related to cyber incidents, and the relevant insurance coverage. 16 Such disclosure requirements triggered the demands for the cyber insurance products because cyber insurance as an effective tool to transfer financial losses or damages could be an evidence that firms are managing cyber security risks well and properly. 17 The demand for cyber-insurance products may be created by government by means of requiring government contractors and subcontractors to purchase cyber insurance under Federal Acquisition Regulations (FAR) which mentions that contractors are required by law and FAR to provide insurance for certain types of perils 18. Also, in order to sustain the covered critical infrastructure (CCI) designation, the owner of such infrastructure may need to carry cyber insurance, too. 19 On the other hand, referring to Support Anti-Terrorism by Fostering Effective Technologies Act of 2002 which requires those who provides Federal and non-Federal Government customers with a qualified/certificated anti-terrorism technologies shall obtain liability insurance of such types but the amount of such insurance shall be reasonable and will not distort the sales price of such technologies 20, the federal government tried to draw and enact legislation that provides limitations on cyber security liability 21. If it works, this could raise the incentive of insurers because amounts of potential financial losses which may be transferred to insurers are predictable. Besides, referring to Terrorism Risk Insurance Act of 2002 which established the terrorism insurance program to provide compensations to insurers who suffered the insured losses due to terrorist attacks 22, the federal government may increase the supply of cyber insurance products by means of providing compensations to insurers who suffered the insured losses due to cyber security breach or cyber attacks. 23 Otherwise, some experts and stakeholders did suggest the federal government implement reinsurance programs to develop cyber insurance programs. 24 Finally, to solve the problem of information asymmetry, the government tried to develop the legislation that could build a mechanism for information-sharing among private entities. 25 Also, it was recommended that the federal government may consider to allow insurance firms to establish an information-sharing database together so that insurers could accordingly develop better models to figure out cyber risks and price the cyber insurance contract accurately. 26 5.Suggestions and conclusion Compared to USA where 30-40 insurers offer cyber-insurance products and thus suggested that a more mature market exists 27, the cyber insurance market in Taiwan is still at the first stage of the product life cycle. Few insurers have introduced their cyber-insurance products covering the issues related to the personal information breach. Per the experience how US government developed the cyber insurance market, the following suggestion are made for reference. First, the government may consider requiring his contractors and subcontractors to carry cyber insurances. This could stimulate the demand for cyber insurance products as well as make cyber insurance prevail among private sector as an effective risk management tool. Second, the government may consider establishing re-insurance program to offer compensation to those who suffer the insured’s large losses and damages or impose limitations of the amount insured by law. However, it is undeniable that providing re-insurance program is not feasible as the government’s budget is not abundance. Finally, an information-sharing mechanism, including information on cyber attacks an cyber risks, may be helpful to solve the problem of information asymmetry. 1.Insurance Act §1 (R.O.C, 2012). 2.European Network and Information Security Agency, Incentives and barriers of the cyber insurance market in Europe , June 2012, at 8, http://www.enisa.europa.eu/activities/Resilience-and-CIIP/national-cyber-security-strategies-ncsss/incentives-and-barriers-of-the-cyber-insurance-market-in-europe. 3.Ben Berkowitz, United States: insurance-cyber insurance, C.T.L.R. 2012, 18(7), N183. 4.Supra note2, at 19-25. 5.Mathew J. Schwartz, Stolen NASA laptop had unencrypted employee data , InformationWeek, November 15, 2012 11:17 AM, http://www.informationweek.com/security/attacks/stolen-nasa-laptop-had-unencrypted-emplo/240142160；Ben Weitzenkorn, Stolen NASA laptop prompts new security rules, TechNewsDaily , November 15 2012 11:35 AM, http://www.technewsdaily.com/15482-stolen-nasa-laptop.html. 6. Irene Klotz, Laptop with NASA workers' personal data is stolen, CAPE CANAVERAL, Nov 14, 2012 8:47pm, http://www.reuters.com/article/2012/11/15/us-space-nasa-security-idUSBRE8AE05F20121115. 7.The Government of the Hong Kong Special Administrative Region , An overview of information security standards, Feb 2008, at 2, http://www.infosec.gov.hk/english/technical/files/overview.pdf；Supra note2, at 21. 8.Supra note2, at 21-22. 9.Id. 10.Id. 11.Id. 12.U.S. Department of Homeland Security, Cyber security insurance workshop readout report, Nov 2012, at 1, http://www.dhs.gov/sites/default/files/publications/cybersecurity-insurance-read-out-report.pdf. 13.John E. Black Jr., Privacy liability and insurance developments in 2012, 16 No. 9 J. Internet L. 3, 12 (2013). 14.Marsh, Number of companies buying cyber insurance up by one-third in 2012, March 14, 2013, http://usa.marsh.com/NewsInsights/MarshPressReleases/ID/29878/Number-of-Companies-Buying-Cyber-Insurance-Up-by-One-Third-in-2012-Marsh.aspx. 15.U.S. Securities and Exchange Commission, CF Disclosure Guidance: Topic No. 2 Cybersecurity, October 13, 2011, http://www.sec.gov/divisions/corpfin/guidance/cfguidance-topic2.htm. 16.Id. 17.Supra note2, at 6.(last visited Dec. 31, 2012) 18.Federal Acquisition Regulations §28.301. 19.E. Paul Kanefsky, Insuring against cyber risks: congress and president Obama weigh in, March 2012, http://www.edwardswildman.com/newsstand/detail.aspx?news=2812. 20.Support Anti-Terrorism by Fostering Effective Technologies Act of 2002 §864. 21.Supra note19. 22.Terrorism Risk Insurance Act of 2002 §103. 23.Supra note19. 24.Id. 25.Id. 26.Id. 27.Supra note2.

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