What Is a Safety Classifier

A safety classifier is a system that evaluates content and assigns it to categories such as safe, unsafe, or requiring review. These classifiers are used to filter both the inputs an AI agent receives and the outputs it produces.

Safety classifiers come in two main forms:

Rule-based classifiers use pattern matching, keyword lists, and regular expressions to detect unsafe content. They are fast, deterministic, and easy to audit. When a rule matches, the classification is always the same. The trade-off is that they miss novel phrasings and can produce false positives on benign content that happens to contain flagged terms.

Model-based classifiers use trained machine learning models to evaluate content semantically. They understand context and can detect harmful intent even when the wording avoids explicit keywords. The trade-off is that they are probabilistic, slower, and harder to debug when they make mistakes.

In practice, production systems use both approaches in layers. Fast rule-based checks handle obvious cases. Model-based classifiers handle ambiguous content that passes the initial rules.

Safety classifiers typically evaluate content across multiple dimensions:

Toxicity. Hateful, threatening, or abusive language directed at individuals or groups.

PII exposure. Social security numbers, credit card numbers, medical records, and other personally identifiable information that should not appear in agent outputs.

Prompt injection. Patterns that suggest an attempt to manipulate the agent's instructions through injected content.

Policy violations. Content that violates organization-specific rules, such as discussing competitors, making unauthorized commitments, or sharing internal information.

Authensor's Aegis scanner is a zero-dependency content safety classifier designed for agent pipelines. It operates as a synchronous check within the policy evaluation flow, scanning both inbound requests and outbound responses. Because it has zero runtime dependencies, it adds minimal latency and can be deployed in any environment without external service calls.

The key consideration when deploying safety classifiers is balancing sensitivity. Too aggressive, and the system blocks legitimate actions. Too permissive, and harmful content passes through.