- Stage 1: A lightweight, efficient classifier screens inbound messages, using minimal power, focused on catching potential threats.
- Stage 2: In-context analysis with our fine-tuned Gemma model, where we analyze a larger snapshot of the conversation to confirm a threat or discard a false alarm.
Built on strict governance
Presented at Singapore International Cyber Week (SICW 2026), this collaboration provides a model for messaging platforms balancing user protection with end-to-end encryption. Strict governance was maintained throughout development:
- Sovereign Sandbox: Fine-tuning took place within GovTech Singapore's managed and maintained GCP environment, keeping training data isolated.
- No Citizen Data: Training strictly excluded private or personally identifiable information, utilising anonymised datasets derived from scam reports and intelligence feeds.
- Stress-Testing: The model was tested against synthetic dialogues and local corpora to cover diverse scam categories and evolving impersonation tactics.
The future of on-device safety
The project demonstrates how AI can help counter criminality without compromising safety or privacy. It also shows how governments and model providers can work together to build tech for public good. With this lightweight, on-device model, we've proven the ability for local, real-time scam detection while ensuring chat interactions remain secure. We look forward to sharing more soon on our collaboration with the Singapore government, as well as exploring the potential for similar technology to be deployed in other countries.