Runtime Guardrails for LLMs and Agentic Systems in 2025

Introduction

As enterprises increasingly deploy large language models (LLMs) and autonomous agentic AI systems, real-time protection against prompt injection, jailbreaks, data leakage, and overprivileged tool misuse becomes critical. Traditional static model guardrails are insufficient to address ever-evolving, complex runtime threats. Runtime guardrails act as a dynamic AI firewall that inspects and controls every prompt, tool call, and model output live, offering an essential layer of security.

This post explores what runtime guardrails are, compares leading vendor solutions including Lakera, Wiz, Cranium, and Arize, and outlines practical patterns for securing AI systems in production.

What Are Runtime Guardrails?

Runtime guardrails are middleware or API layers positioned between users, tools, and AI models that:

Detect and block adversarial prompts such as prompt injections and jailbreaks
Filter outputs to prevent exposure of sensitive or confidential data
Vet agent tool invocations and arguments to enforce policy restrictions
Provide logging, observability, and integration into SIEM and incident workflows

Unlike static model alignment, runtime guardrails monitor live AI operations to prevent new, unknown, or sophisticated attacks dynamically during inference.

Leading Runtime Guardrail Solutions

Lakera Guard: Model-Agnostic AI Firewall

Lakera Guard operates as a real-time “AI firewall” wrapping around existing LLM APIs, detecting sophisticated prompt injection and jailbreak attempts, controlling data leakage by scanning input/output strings for sensitive information, and inspecting agent tool calls. It is highly flexible, supporting multi-cloud and self-hosted model environments with low-latency processing suitable for conversational agents.

Learn more: Lakera Guard Official Website

Wiz: Cloud-Native Runtime AI Security

Wiz Security Graph focuses on runtime detection of AI inference threats and exposure risk prioritization. It provides threat correlation, continuous monitoring across hybrid clouds, and automates incident response by integrating with SecOps tools. Wiz excels in AI governance and compliance for cloud workloads.

Learn more: Wiz AI Runtime Security

Cranium: AI Exposure Management & Automated Remediation

Cranium’s AI security platform enriches runtime guardrails with system context for attack surface mapping, continuous risk assessment, and automated mitigation workflows. It fits into existing enterprise frameworks with deep AI pipeline visibility and dynamic vulnerability management.

Learn more: Cranium AI Exposure Management

Arize AI: Observability Plus Guardrail Enforcement

Arize blends ML observability with customizable runtime guardrails, enabling real-time validation and corrective actions on LLM outputs. This combination helps organizations maintain output safety and performance insight simultaneously, useful for complex production deployments requiring auditability.

Learn more: Arize AI Guardrails Documentation

Other Notables

Amazon Bedrock Guardrails for AWS-native AI governance
Cloud platform filters from OpenAI, Azure, and GCP for baseline protection
Third-party tools like NeuralTrust and Fiddler offering specialized guardrail features

Implementing Runtime Guardrails: A Three-Layer Architecture

Pre-Prompt Gate Inspect incoming prompts for injections or malicious content. Block or rewrite unsafe prompts before model invocation.
Tool-Call Gate (For Agentic Systems) Vet and sanitize inputs to external tools (e.g., databases, HTTP calls) used by AI agents, enforcing policy on allowed commands.
Post-Response Gate Scan outputs for disallowed content, secrets, or confidential data. Apply redaction or blocking as configured.

Integration to existing LLM frameworks (LangChain, Semantic Kernel) typically involves wrapping calls to models and tools with guardrail checks and capturing detailed logs for response tuning and incident analysis.

Why Runtime Guardrails Matter

Dynamic runtime guardrails catch threats unseen by static methods, reducing breached data risk and unauthorized tool use in AI workflows. Benchmark tests show these guardrails achieve high accuracy in prompt injection detection and low latency for real-time applications. By combining runtime guardrails with model alignment and secure development practices, organizations build robust, compliant AI deployments that innovate safely.

Additional resources: OWASP AI Security Project