Safety & Security · Software component
Domain Compliance Rail
Software componentSafety & SecuritySafety, Security & GovernanceVariation point (abstract)arc:DomainComplianceRail
An abstract output guardrail that checks a proposed response against domain-specific regulatory rules and, on violation, halts delivery and returns a standardized refusal with a logged reason.
Responsibility. Blocks outputs that violate domain regulations.
Also known as: check financial compliance flow, Compliance policy guardrail
Variants
| Variant | When to choose |
|---|---|
| Pattern Compliance Checker | Choose as a fast, deterministic check for explicit regulated phrasing (e.g., 'you should buy', price predictions); insufficient alone because paraphrased advice evades surface patterns. |
| Semantic Compliance Classifier | Choose when adversaries or the model paraphrase regulated content, so advice-giving intent must be detected regardless of phrasing, accepting higher false positives and compute cost. |
Relationships
is configured by structural
invokes dependency
emits telemetry to dynamic
guards control
is orchestrated by control
Design guidance
- MUST stop normal processing on violation so partially compliant responses never reach the user.
- MUST log the violation reason to demonstrate due diligence to regulators.
- SHOULD separate policy (what to filter) from mechanism (how) so compliance teams can change policy without engineering involvement.
Classification
- Technologies
- NeMo GuardrailsColang
- Risks mitigated
- Unlicensed investment advicePrice predictionsRegulatory violations and fines
- Frameworks & regulations
- FINRA rules on investment communicationsFCRA (Fair Credit Reporting Act)Fair lending lawsEqual credit opportunity requirements
Sources
- Ch9.1: T. Nguyen, "Output Filtering and Content Moderation," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.1. ISBN: 9798244538229.
- Ref9.04: "Safety Guardrails Implementation for Agent Systems," unpublished reference note (04-Safety-Guardrails-Implementation.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note