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Use Cases#

This page groups practical Admin UI scenarios and common use cases supported by the OptScale AI. The examples below illustrate how the platform fits into different workflows and supports everyday operational and development tasks.

Policies + Guardrails#

Table 1: OptScale AI policies and guardrails - common examples
Use case Typical guardrails Policy pattern
Redact PII in user prompts PII detection and redaction on Input, action Redact Match Chat Completion (or your API request type); Stage Input; Sampling rate 100% for compliance-sensitive workloads
Block credential leaks Secrets on Input and/or Output Organization-wide or team-scoped conditions; block or redact before content reaches the provider or the client
Restrict unsafe or off-topic content Ban topics, Toxicity, or Sentiment Scope by Request type, Provider, or Team when only certain workloads need stricter rules
Harden against prompt abuse Prompt injection, Jailbreak, Invisible text on Input Apply to externally facing Chat or API traffic; start with moderate thresholds and tune from Violation rate
Control response safety Toxicity, Code injection, or Secrets on Output Stage Output so model responses are checked before they return to the user or calling application
Limit oversized requests Token limit Match high-volume or untrusted entry points; enforce limits before expensive provider calls
Layered governance Multiple guardrails on one policy (for example PII detection and redaction + Secrets on Input) One policy with shared Conditions; each linked guardrail keeps its own Type, Threshold, and action

Reuse guardrails across policies when the same control applies to different scopes (for example one Secrets guardrail linked from both a Chat policy and an API policy with different conditions).