Singapore is one of the most AI-friendly places in the world to do business, with a national AI strategy, active industry support and clear guidance from regulators. That guidance comes with an expectation: AI systems that use personal data should be built responsibly from day one. Here is a practical checklist we use when building AI for Singapore companies. It is not legal advice, so involve your Data Protection Officer early.
Start with the PDPA basics
The Personal Data Protection Act 2012, enforced by the Personal Data Protection Commission (PDPC), applies whenever your AI system collects, uses or discloses personal data. The key obligations translate directly into engineering decisions:
- Consent and purpose: only use personal data for purposes customers were told about. Training a model on support tickets collected for "handling your request" may need a fresh look.
- Minimisation: strip or mask names, NRIC numbers, phone numbers and emails before data reaches a model wherever you can.
- Protection: encrypt data at rest and in transit, restrict access by role and keep audit logs of who queried what.
- Retention: decide how long prompts, conversation logs and embeddings are kept, and delete them on schedule.
- Transfers: if data leaves Singapore, for example to an overseas model provider or development team, the PDPA's transfer obligations apply.
Use the Model AI Governance Framework as your design brief
The PDPC and IMDA publish a Model AI Governance Framework, with later guidance covering generative AI. It is voluntary, but it is the standard regulators, customers and partners will measure you against. Its principles map neatly onto product requirements: decisions should be explainable, there should be human oversight proportionate to risk, and the people affected should know when they are dealing with AI.
Test with AI Verify
Singapore's AI Verify toolkit helps organisations test AI systems against recognised governance principles and produce a report they can share. Running it before launch gives your compliance team and enterprise customers evidence, not just promises, that the system was tested for issues such as fairness and robustness.
Choose hosting and model providers deliberately
- Host applications, vector databases and logs in a Singapore cloud region where possible.
- Use enterprise LLM agreements that exclude your data from model training and set clear retention limits.
- For the most sensitive workloads, consider open-source models deployed on your own infrastructure.
Working with an offshore team
Many Singapore companies build AI with remote teams to control cost. That works well when the contract and setup are right: a data processing agreement, access to masked or synthetic data during development, production data that stays in your Singapore environment, and named people with clear access rights. A capable partner will raise these points before you do.
Make governance part of the build, not an afterthought
Retrofitting consent flows, audit logs and explanations after launch costs far more than designing them in. KenStack works with Singapore companies as a remote custom AI development team, and we scope data handling, hosting and governance in the first week of every project.
