Debugging False Positives in AI Safety Scanners

False positives are the most common operational issue with content safety scanners. When a scanner incorrectly flags benign content as unsafe, the agent either blocks a legitimate action or escalates it unnecessarily. Too many false positives erode trust in the safety system and lead teams to disable scanning entirely.

Identifying False Positives

Start by reviewing denied or flagged actions in your audit trail. For each flagged item, ask:

1. Was the content actually harmful or policy-violating?
2. Which specific detection rule triggered?
3. What pattern in the content matched the rule?

Authensor's Aegis scanner includes detection metadata in the audit receipt. The detection_rule, matched_pattern, and confidence_score fields tell you exactly what triggered and why.

Common Causes

Overly broad regex patterns. A rule designed to catch SQL injection might flag legitimate database documentation that discusses SQL syntax. The pattern DROP TABLE matches both an attack payload and a tutorial about database management.

Context-free keyword matching. Words like "kill" (as in "kill the process"), "execute" (as in "execute the function"), or "injection" (as in "dependency injection") are benign in technical contexts but may trigger safety rules.

Low confidence thresholds. If detection thresholds are set too low, borderline content is flagged. A threshold of 0.3 will catch more true positives but also more false positives than a threshold of 0.7.

Resolution Strategies

Add context-aware exceptions. Instead of removing a rule, add exceptions for known safe contexts. Allow "kill" when followed by "process" or "signal." Allow "DROP TABLE" when the content type is documentation.

Raise confidence thresholds. Increase the minimum confidence score required to trigger a detection. Monitor the impact by tracking whether true positives are missed at the new threshold.

Use allowlists for trusted content. Content from verified internal sources can bypass specific rules. This is especially useful for developer documentation and technical training materials.

Review and tune regularly. Schedule monthly reviews of false positive rates. Pull the top 10 most frequently triggered rules and evaluate whether each one is still correctly calibrated.

Never disable a safety rule without understanding what it protects against. The correct response to false positives is tuning, not removal.