When implementing prompt injection detection, control over false positives is a compliance requirement, not just an engineering preference. A high false‑positive rate can disrupt legitimate user workflows and create audit log noise that obscures genuine incidents.

Vectara's Gated LLM offers a managed service with predefined classifiers. A DIY approach allows you to tailor detection logic and thresholds.

Key considerations for control:
* **Threshold tuning**: Can you adjust sensitivity per use case (e.g., internal tool vs. public chatbot)?
* **Rule granularity**: Can you create allowlists for known safe patterns or contexts?
* **Logging & feedback loop**: Does the solution provide sufficient detail to investigate and refine decisions? For SOX or GDPR, you must document the rationale for each block.

In your experience, which architecture—managed service or custom classifier—provides the necessary levers to keep false positives within an acceptable risk tolerance for regulated workloads?