Domyn
LLM Guardrails

From Black Box to
Clear Boundaries

LLM guardrails are the control mechanisms  that make LLMs safe, aligned, and reliable. They define and enforce boundaries on what a model can generate, preventing harmful outputs, protecting sensitive data, and ensuring responses meet ethical, regulatory, and business standards.

The Structural and Behavioral Risks of Deploying LLMs Without Guardrails

Positional bias
Framing sensitivity
Fabricated financial data
Hallucinated regulatory citations
Miscalculated risk metrics
Incorrect portfolio allocation math
Misinterpretation of loan terms or covenants
Wrong currency conversions
Inconsistent answers to the same financial query
Fabricated correlations between assets
Misclassification of financial instruments
Positional bias
Framing sensitivity
Fabricated financial data
Hallucinated regulatory citations
Miscalculated risk metrics
Incorrect portfolio allocation math
Research & evaluation

Validating Guardrails Against Real-World Risk

Two separate benchmarks assessed guard models across core risk dimensions: hallucination detection, grounding validation, as well as NIST-aligned unsafe content classification.

Hallucination Benchmark

Structured evaluation of hallucination detection and
groundedness assessment across established datasets.

Safety Benchmark

Structured evaluation of unsafe content detection across eight
safety categories aligned with NIST
it