On May 14, 2026, Thailand published a ministerial regulation in the Government Gazette to prescribe measures for prevention and suppression of technology crimes. The regulation creates a comprehensive procedural framework for returning money and digital assets to victims of technology crimes. It will take effect 90 days after publication (in mid-August 2026), giving affected entities a limited window to prepare. Mandatory Reporting Obligations for Financial Institutions When a deposit account, e-money account, or digital asset wallet is frozen in connection with a technology crime, the relevant financial institution or business operator must report transaction data to the Anti-Money Laundering Office (AMLO) via AMLO’s designated electronic system. Required data elements include account numbers (sender and receiver), names, identification or passport numbers, legal entity registration numbers, phone numbers, remaining balance, damage amount, transaction reference numbers, and the bank case ID. Institutions that already share data through the information-sharing system under the emergency decree are deemed to have satisfied this reporting obligation, creating an incentive for platform participation. When the Royal Thai Police or the Department of Special Investigation seize or freeze assets related to technology crimes, they must provide AMLO with investigation reports, complaint evidence, money-trail data, and account statements. Notification and Claims Process Once the AMLO secretary-general approves verified reports of a technology crime, the account information of persons connected to the crime will be published in the Government Gazette, triggering a 90-day window for victims to file claims and for related persons to file objections. Officers will also publish details on AMLO’s electronic media and send registered mail to identified victims, which will be deemed received after 7 days domestically or 15 days internationally. Victims have 90 days from the date the crime is published in the Government Gazette to file claims through AMLO’s electronic system. Claims must include

For the first time, Thailand’s Food and Drug Administration (FDA) has published a consolidated list identifying all substances that have successfully passed its novel food safety evaluation process. The list is a step forward in regulatory transparency, but it also highlights a feature of the Thai regime that food companies often overlook: each approval is tied exclusively to the company that applied for it. A substance’s appearance on the list does not give other companies the green light to use it. This article examines the structure of Thailand’s novel food approval framework, the implications of applicant exclusivity, and the strategic choices it requires of food companies looking to bring novel ingredients to the Thai market. Thai FDA Food Safety Evaluation Framework Notification No. 376 of the Ministry of Public Health requires novel food substances to undergo a food safety assessment, with an exemption only for novel foods manufactured exclusively for export. The framework also encompasses “foods that do not qualify as novel foods” but which present characteristics warranting a safety evaluation, such as differing quality standards, increased serving sizes, or applications in specific food categories, where such changes affect consumption levels, nutritional value, or consumer safety. The recently published list of foods that passed the safety evaluation by the Thai FDA is structured by substance category and identifies the approved company (domestic manufacturer or importer), country of origin, substance name and trade name, approved purpose of use, and date of the Thai FDA’s approval notification certificate. A notable feature of Thailand’s novel food regime is that the approval result is tied exclusively to the company that submitted the application. Publication of the consolidated list does not constitute a general authorization to use the approved substances. The Thai FDA’s approval certificate specifies the approved conditions of use and the requirements

Thailand’s new labeling requirements for medical devices, which include for the first time a unique device identification (UDI) requirement for software as a medical device (SaMD), take effect on June 20, 2026. The Notification of the Ministry of Public Health regarding Criteria, Methods, and Conditions on Labeling and Instructions for Use for Medical Devices 2025, which replaces a similar notification from 2020, was published in the Government Gazette on December 22, 2025. To ensure clarity, modernity, and patient safety, the regulation requires domestic manufacturers and importers to provide labels and instructions for use (IFU) that are clearly legible, complete, and free of false or misleading claims. It also permits IFU to be provided in electronic format, such as via QR codes, websites, or other digital channels—directly relevant to SaMD, where physical labels are impractical and electronic presentation is the natural medium. The notification distinguishes two categories for labeling language. Home-use medical devices (for lay users outside healthcare facilities) must have labels and IFU in Thai. Professional-use medical devices may display labels and documentation in either Thai or English. This distinction is significant for SaMD developers: software intended for clinical professionals may use English-language interfaces and IFU, while consumer-facing health applications must provide Thai-language content. Labeling and UDI Requirements Labels and IFU must include, at a minimum: Product name and intended purpose Quantity or volume Name and address of domestic manufacturer or importer Thai FDA approval number Lot, version, or serial number Manufacturing date and expiry date For SaMD, the version number requirement is particularly relevant. The regulation also mandates display of a UDI code for SaMD in risk category 2 (moderate-risk), category 3 (moderate- to high-risk), and category 4 (high-risk), according to Thailand’s medical device risk classification system (which complies with the ASEAN Medical Device Directive and the EU

The surge in AI development has led to a desperate demand for large, high-quality training data. However, real-world data can be expensive to collect, difficult to access, and often subject to strict privacy and regulatory constraints. Synthetic data, which consists of artificially generated records that replicate the statistical properties of real-world data without reproducing specific individuals’ information, provides an appealing solution by generating artificial datasets at scale without relying on identifiable personal information. It combines speed, cost efficiency, and regulatory compliance, making it a sensible alternative for organizations seeking to reduce risks while maintaining data utility. When properly anonymized, synthetic datasets may fall outside the scope of laws such as the EU’s General Data Protection Regulation (GDPR) or Thailand’s Personal Data Protection Act (PDPA), reducing compliance burdens while still supporting high-quality model training. However, relying on synthetic data without rigorous legal due diligence could be a strategic mistake. It replaces one set of known risks (scraping, direct privacy liability) with a new set of complex liabilities. The narrative that synthetic data is a “silver bullet” for privacy and IP compliance is dangerous and could be misleading. While synthetic data addresses data scarcity, it also introduces new legal uncertainties. Legal counsel should anticipate downstream risks arising from compromised data sources. Models trained on unlawfully obtained data may need to be decommissioned, even if their outputs appear lawful. What is synthetic data? Synthetic data refers to artificially generated information created using AI techniques such as deep learning and generative models. Instead of copying real records, it reproduces the statistical patterns and relationships found in the original dataset. Synthetic data generally falls into three categories: Fully synthetic data – Entirely new data points generated from learned patterns. The model studies the structure of the original data and produces records that resemble real-world