Large Language Models (LLMs) have achieved strong performance across diverse NLP tasks, but deploying them in real-world scenarios raises concerns regarding safety, reliability, and computational cost. To mitigate the risk of harmful, unreliable, or unfaithful content, LLM pipelines increasingly rely on guardrails. Yet deploying multiple guardrails across different tasks and risk categories introduces significant computational and orchestration overhead. In this work, we propose a calibrated deferral-routing framework for deploying guardrails both accurately and cost-effectively. Our approach arranges guardrails as a cascade of increasing capability and cost: each input is first evaluated by a lightweight guardrail, while only uncertain instances are deferred to a larger, more capable, and more expensive model. We formalize this routing problem and study practical deferral-calibration strategies by applying post-hoc calibration, including isotonic regression, Platt scaling, and temperature scaling. We evaluate 18 guardrail models from four model families (i.e., Granite Guardian, Llama Guard, ShieldGemma, and GuardReasoner) across 10 harm-detection benchmarks. Our experiments show that: (i) all guardrail models are often poorly calibrated out of the box (ECE ranging from 6% to 28%); (ii) post-hoc calibration substantially improves reliability reducing ECE below 2.5% in most cases; (iii) cascade routing yields large cost savings with minimal accuracy loss, while monotone calibration primarily makes routing thresholds deployable rather than improving rank ordering.

The deployment of language models in real-world applications exposes users to various risks, including hallucinations and harmful or unethical content. These challenges highlight the urgent need for robust safeguards to ensure safe and responsible AI. To address this, we introduce Granite Guardian, a suite of advanced models designed to detect and mitigate risks associated with prompts and responses, enabling seamless integration with any large language model (LLM). Unlike existing open-source solutions, our Granite Guardian models provide comprehensive coverage across a wide range of risk dimensions, including social bias, profanity, violence, sexual content, unethical behavior, jailbreaking, and hallucination-related issues such as context relevance, groundedness, and answer accuracy in retrieval-augmented generation (RAG) scenarios. Trained on a unique dataset combining diverse human annotations and synthetic data, Granite Guardian excels in identifying risks often overlooked by traditional detection systems, particularly jailbreak attempts and RAG-specific challenges. https://github.com/ibm-granite/granite-guardian