Singapore gives businesses a clearer path for using data in AI

Nurdianah Md Nur

Jul 20

PDPC and IMDA set out guidance on GenAI data use, federated learning, synthetic data and digital twins, with use cases from SGH, Ant International and Exceltec.Singapore has issued new guidelines and ...

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Garbage in, garbage out. That is why data governance matters more, not less, in the age of AI,\" says Minister for Digital Development and Information Josephine Teo at the Singapore Data Festival 2026.\nThe first set of rules addresses generative AI, in which companies increasingly rely on large volumes of data to build and deploy models.\nThe Personal Data Protection Commission's (PDPC) guidelines clarify how the Personal Data Protection Act (PDPA) applies to personal data used in generative AI (GenAI) models and systems. They cover web-scraping, the reuse of data originally provided for non-GenAI purposes, the responsibilities of different GenAI stakeholders, and how organisations should respond to individual requests concerning their data.\nCompanies developing GenAI models may rely on the PDPA's \"Publicly Available Exception\" to scrape publicly accessible personal data without consent.\nHowever, the guidance draws a line between open information and data placed behind digital barriers. Where personal data sits behind paywalls or registration requirements, organisations must assess whether it still qualifies as publicly available.\nThe rules are more explicit when companies reuse user data that was collected for other purposes. If no consent exception applies, organisations must obtain consent through \"AI-Specific Notifications\". These notices should explain why the data is being used, what information is involved, how it will be used, and how individuals can decline or withdraw consent.\n\"Organisations should say so plainly rather than rely on broad descriptions that users may not notice or understand,\" says Teo.\nPDPC has also divided responsibility across the GenAI supply chain. Model providers must comply with PDPA obligations when processing data to develop and deploy GenAI models. System providers must periodically review system-level security arrangements.\nAs for system deployers, they bear primary responsibility for PDPA compliance, including defining the purpose of personal data processing, protecting data flows, and reviewing safeguards, especially for agentic AI systems.\nIndividuals can request access to and correction of their personal data even after it has been used in GenAI development. PDPC says organisations are expected to adopt practices such as upstream data handling, case-by-case reviews and appropriate technical measures.\nThe guidelines incorporate feedback from a public consultation that closed on July 1. PDPC received responses from 40 organisations, including Google, Meta, WeChat, DBS, Singapore Airlines, Workday, Prudential, HSBC, Epic Systems Corporation and NUHS, as well as three individuals.\nTraining AI without pooling sensitive data\nBesides that, PDPC launched a guide on federated learning, which is a privacy-enhancing technology (PET) for situations where useful data is siloed, sensitive or regulated. Instead of centralising data, federated learning lets organisations develop AI models by extracting relevant data and insights while the underlying data stays where it is.\nThe guide intends to help businesses decide whether federated learning fits their needs, what adoption would require, how a solution should be configured and what risks must be managed.\nPDPC also updated its guide on synthetic data generation, which creates artificial datasets that mirror the statistical properties of real data without containing individuals' personal information. The updated guide expands on generation methods, best practices to prevent re-identification and case studies of practical applications.\nAccording to IMDA, its PET Sandbox has supported 11 organisations across finance, healthcare, construction, transport and advertising technology.\nFor instance, Singapore General Hospital used the sandbox to assess whether medical images could be stored and analysed securely in the cloud. The hospital used a trusted execution environment solution from A*STAR's Institute of Advanced Intelligence and Computing, allowing digitised scans to be processed in a secured cloud environment to support clinical diagnosis.\nMeanwhile, Ant International used the sandbox for payments and risk controls. The company assessed multi-party computing to reduce the collection and storage of potentially sensitive data from merchants, acquirers and wallet partners, while still supporting transaction verification and marketing discount eligibility checks.\nTo encourage wider adoption, IMDA will add practical resources that allow organisations to explore demonstrations of privacy-enhancing tools before testing and implementing their own use cases.\nDigital twins for non-tech companies\nSeparately, IMDA launched what it says is \"Singapore's first Digital Twin for Enterprises Playbook\", aimed at helping non-ICT companies evaluate and deploy the technology using practical frameworks and local case studies.\nA digital twin is a live, data-driven replica of a physical asset, system or process that stays synchronised with real-world operations. This could mean a virtual version of a building, machine or logistics network that reflects what is happening on the ground. Such technology can bring operational data into a single view, help companies monitor performance, anticipate issues when integrated with AI, and test changes before implementation.\nThe playbook was developed with input from early adopters, solution providers and technology leaders. It draws on deployment experience in manufacturing, the built environment, logistics and supply chains, where downtime, defects and disruptions can be costly. It also guides companies through use-case selection, data readiness, safeguards and execution.\nIts implementation section sets out five areas of organisational readiness: leadership, technology and data, operations, employees and new market opportunities. It also outlines a four-step rollout covering use-case definition, data readiness, governance and cybersecurity safeguards, and staged deployment. Companies are advised to start with monitoring before adding prediction and simulation.\nOne case study is Exceltec Property Management, which manages more than 100 sites across Singapore. Before adopting a digital twin, its buildings ran on separate systems. Data was fragmented, information was gathered manually and inspections followed fixed schedules.\nUnder IMDA's Digital Leaders Programme, Exceltec combined data from Internet of Things (IoT) sensors, building systems and maintenance logs into a unified platform. The system gave the company real-time visibility across its managed sites, which allowed cleaning crews to be deployed based on usage patterns and enabled AI models to detect early signs of pump faults. As a result, manual pump inspections were eliminated, saving about 45 minutes of technician time daily, according to IMDA.\nExceltec is also using the data to give clients insights for long-term capital planning. It is exploring ways to commercialise the solution with partners such as cleaning firms.\nAccording to Teo, the above-mentioned initiatives are part of a wider effort to build trusted data and AI ecosystems beyond Singapore.\n\"None of us can build a trusted data ecosystem by looking only within our borders,\" she says, adding that Singapore will work with regional partners as it assumes the ASEAN chairmanship next year to bring approaches closer together, reduce unnecessary friction for businesses and create more room for digital economies to grow.","kind":"highlight","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":false,"editions":null,"column":"","sentiment":"0","news_tag":"","news_rank":0,"isVideo":false,"video":null,"symbols":[],"gpt_button":0,"need_auth":false,"need_login_tip":false,"code":"91000000","status":"200"}}}