Safety Classifier

A safety classifier is a smaller specialized model that screens LLM inputs and outputs for harmful, toxic, or policy-violating content, typically deployed as a pre- or post-processing layer around a generative LLM. Major providers offer safety classifiers including OpenAI Moderation API, Google's Perspective API and Vertex AI Safety Filters, Azure AI Content Safety, AWS Comprehend Toxic Content Detection, and Meta's Llama Guard family. Open-source alternatives include Llama Guard 3, NeMo Guardrails, and various Hugging Face moderation models. Safety classifiers complement model-level refusal training by providing a deterministic policy enforcement layer that can be tuned independently of the generative model, configured per audience or jurisdiction, and audited against measurable false-positive and false-negative rates. AI governance teams document the safety classifiers in their deployment alongside the base model because the combined system's safety properties depend on both layers. The trade-offs include latency (classifier adds 50-200ms), cost (separate model serving), false positives (legitimate content blocked), and false negatives (harmful content passing through).

Safety classifiers in Centralpoint: Centralpoint integrates safety classifiers from OpenAI, Google, Llama Guard, and other sources as pre- or post-processing layers around any LLM in a model-agnostic stack. Tokens are metered per skill, prompts stay local, and policy-enforced chatbots deploy through one line of JavaScript on any portal.


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