SEA-Guard

SEA-Guard is currently a collection of safety-classification models. It classifies user requests or model responses as safe / unsafe and supports text plus some vision-text scenarios.

It is not a universal replacement for human review. Its role is to give Southeast Asian application developers a more localized first guardrail: when they connect a general LLM or SEA-LION, SEA-Guard can screen risks through a regional-cultural lens.

AI safety models are often strongest in English and US cultural contexts. Southeast Asia is more complex: multi-religious, multi-ethnic, and multilingual, with local harms and offence patterns that may not appear in English safety datasets.

SEA-Guard matters because it regionalizes safety alignment too. It asks a local language model not only to speak local languages, but also to understand local boundaries.

SEA-Guard connects two Singapore AI lines: SEA-LION’s regional-model path and AI Verify’s trustworthy-AI governance path.

If SEA-LION is to enter sensitive sectors such as government, education, healthcare, and finance, safety guardrails are not a side feature; they are a deployment precondition. SEA-Guard is that precondition at the model layer.