Classifier Based Content Safety Scanning

Classifier-based content safety uses machine learning models trained specifically to detect harmful content categories. Unlike regex patterns that match syntax, classifiers understand semantic meaning. They are the workhorses of production content safety systems.

How Safety Classifiers Work

A safety classifier takes text input and produces probability scores across categories like hate speech, self-harm, sexual content, violence, and dangerous instructions. These scores are compared against thresholds to determine whether content is safe.

Training data consists of labeled examples of safe and unsafe content across each category. Models range from lightweight BERT-based classifiers that run locally to API-based services from model providers.

Choosing a Classifier

Self-hosted classifiers like those based on ModerateHatespeech or ToxicBERT give you full control. They run on your infrastructure with no data leaving your network. Latency is predictable. The tradeoff is that you maintain the model and its accuracy depends on your training data.

API-based classifiers from OpenAI, Google, or Anthropic leverage massive training datasets and continuous updates. They are more accurate for general content but introduce external dependencies, network latency, and data privacy considerations.

Hybrid approaches run a fast local classifier first and escalate borderline cases to an API classifier. This balances speed, accuracy, and cost.

Integration Patterns

Place classifiers at two points in the agent pipeline: after user input arrives and before agent output is returned. Authensor's Aegis scanner supports pluggable detection strategies, letting you configure which classifiers run at which stage.

Set category-specific thresholds based on your risk tolerance. A healthcare application needs stricter thresholds for medical advice than a creative writing tool. Authensor's policy language lets you define per-category thresholds and actions.

Monitoring Classifier Performance

Track false positive and false negative rates. Log classifier decisions alongside confidence scores. Review flagged content periodically to identify drift. Authensor's audit trail captures every safety decision, giving you the data needed to tune classifier performance over time.