AI Input and Output Classifiers

An AI classifier is a safeguard that assigns a label or risk score to content and uses a policy threshold to decide how the system should respond. In generative AI, input classifiers screen user-supplied content before generation, while output classifiers examine the model's response before it reaches the user. They moderate interactions but do not eliminate the underlying model's risks.

Filtering the input

The system first evaluates the prompt and, where relevant, uploaded text or other content against defined risk categories. A risk score above a chosen threshold can trigger a predetermined action.

• The classifier may detect prohibited content, attempts at prompt injection, malicious instructions, or sensitive information.
• Depending on policy, the system may allow, block, redact, request clarification, or route the interaction for human review.
• Rules and keyword matching may be combined with machine-learning classifiers because harmful meaning often depends on context rather than individual words.

Filtering the output

After the generative model produces a candidate response, an output filter assesses it for unsafe, disallowed, or privacy-sensitive content. The response may then be released, modified, suppressed, or regenerated.

• Output screening is necessary because an acceptable prompt can still produce an unsafe or policy-violating answer.
• Separate checks may classify different risks, such as harmful content, disclosure of personal information, or insecure computer instructions.
• Thresholds balance protection against false positives, which block acceptable content, and false negatives, which allow harmful content.

Limits and layered safeguards

Classifiers are imperfect because language is contextual and attackers can use obfuscation, indirect wording, or unfamiliar languages. Their performance can also decline under distribution shift, when real inputs differ from training and evaluation data.

• Classifiers do not by themselves guarantee factual accuracy, fairness, privacy, or resistance to adversarial manipulation.
• Effective protection combines filters with access controls, secure system design, constrained tool permissions, testing, monitoring, logging, and human oversight.
• Regular evaluation should measure failures across languages, user groups, content types, and plausible adversarial inputs.