Adaptive AI guardrail boosts safety and policy compliance in enterprise apps
AdaGuard: Enhancing Safety and Policy Compliance with Reasoning-Enabled LLM-As-A-Judge Guardrails
Artificial Intelligence
Summary
Keeping AI tools safe and following rules is hard because different jobs need different rules and speeds. The authors built AdaGuard, an AI system that acts like a judge to check if AI outputs follow the right rules. It smartly decides when to take more time to explain its decisions or work quickly, helping companies stay safe without slowing down. This system learns to handle new rules on the fly without being retrained often.
What this means in practice
- •For enterprise software developers: Build AI applications that enforce evolving safety and compliance policies with adaptive speed and explanation trade-offs.
- •For customer support teams: Use adaptive AI moderation to ensure policy compliance in automated response systems without sacrificing response time.
Authors
Melissa Kazemi Rad, Sihui Dai, Isha Slavin, Kushal Chawla, Mann Patel, Jian Ni, William M. Campbell, Stephen Rawls, Sambit Sahu
Abstract
Enterprise generative AI applications require robust safety mechanisms that can accommodate diverse risk postures, evolving policies, and varying latency constraints. Current guardrail solutions often suffer from rigidity, relying on fixed policy sets and offering limited transparency or reasoning flexibility. We present Adaguard, an adaptive LLM-as-a-Judge framework designed to address these challenges through dynamic policy enforcement and adaptive reasoning-budget allocation. Built using supervised fine-tuning (SFT) and reinforcement learning (GRPO), AdaGuard generalizes to user-defined safety and compliance policies at runtime without requiring frequent model updates. A core innovation of our approach is the ability to dynamically infer the complexity of input-policy pairs, allowing the model to switch between high-speed black-box inference and explainable, reasoning-enabled moderation. This flexibility enables developers to balance stringent latency requirements with the need for actionable transparency. This adaptive capability allows AdaGuard to rival other guardrail and frontier models several times its size, while its auto-reasoning mode recovers the accuracy of always-on reasoning at a fraction of the latency