The table below documents every guardrail surfaced byDocumentation Index
Fetch the complete documentation index at: https://cloudsineai-5cd7c547.mintlify.app/llms.txt
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input-check and output-check, the engine class behind it, and the phase(s) in which it operates.
| Guardrail | Phase | Engine | Description |
|---|---|---|---|
| LLM | Input + Output | LLM-as-judge | Scores prompt-injection likelihood 0–1 using a model-based judge. |
| Keyword | Input + Output | Rule-based | Exact keyword/phrase blocklist match. |
| Regex | Input + Output | Rule-based | Regex pattern match against operator-configured patterns. |
| PII Detection | Input + Output | NER model | Detects names, emails, phone numbers, NRIC/passport, credit cards, etc. |
| Vector | Input | Semantic similarity over TVDB | Similarity search against the proprietary Threat Vector Database. Catches paraphrased injection. |
| Content Moderation | Input + Output | Purpose-built classifier | Classifies harmful content (violence, hate speech, unethical, etc.). |
| System Prompt Protection | Output | In-line detection | Detects when the LLM is leaking its system prompt in its response. |
When each guardrail fires
injection_detected is true in the API response if any active guardrail flags the input or output. Use the per-guardrail block within checks to identify which layer produced the decision and tune accordingly.
Configuring sensitivity
| Guardrail | Tunable |
|---|---|
| LLM | Threshold (per security profile) |
| Vector | Sensitivity (Low / Medium / High) |
| Content Moderation | Category-level enable/disable |
| System Prompt Protection | Automatic in Forwarding mode (no application-side configuration required) |
| Keyword / Regex | Operator-defined rule sets |
| PII | Per-category toggles (NRIC/FIN, Phone, Email, Person, Credit Card, etc.) |