Persona Guardrail: A Production-Grade Defense Framework for Agentic Systems
Persona Guardrail is a runtime defense framework for LLM agents that checks both user inputs and agent outputs against explicit semantic allowlists/blocklists so customer-facing agents stay within their intended functional scope rather than just reacting to known prompt-injection patterns. What’s distinctive is the persona/function-specific framing plus the PAGE benchmark and production deployment evidence—showing much better out-of-domain detection and low latency than a generic LLM guardrail—though it likely depends heavily on well-written, maintained boundary specs and may not generalize automatically to messy or rapidly changing agent roles.
Abstract
Large language model-based agents are increasingly deployed to perform domain-specific tasks by interacting with enterprise knowledge, tools, and external services. Existing runtime guardrails primarily target prompt injection and other attack-specific behaviors under a black-box threat model, but provide limited guarantees that agents operate within their intended functionality. As a result, production agents remain vulnerable to malicious requests and out-of-domain queries that existing defenses often fail to distinguish. We present Persona Guardrail, a production-grade runtime defense framework that enforces explicit functional boundaries for customer-facing agentic AI systems through synchronous input and output validation driven by semantic allowlist and blocklist specifications. We also introduce PAGE (Persona-Aware Guardrail Evaluation), a benchmark for evaluating function-specific guardrails across benign, adversarial, and out-of-domain interactions on both user and agent turns. Compared with a generic LLM-based guardrail, Persona Guardrail improves overall accuracy from 85.7% to 95.9%, increases out-of-domain detection from 57.3% to 93.5%, and reduces the false-approved rate from 25.0% to 4.7%. Currently deployed in production, Persona Guardrail meets its latency budget while sustaining a very low false-block and false-allow rate under realistic production workloads. These results demonstrate that Persona Guardrail provides a practical, scalable, and production-ready foundation for securing agentic AI systems.