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An open-source AI security framework for production environments that addresses post-deployment behavior monitoring, prompt injection protection, and agent drift issues through a four-layer architecture: Guardrails, Model-as-Judge, human supervision, and circuit breaker mechanisms.
Section 01
An open-source AI security framework for production environments that addresses post-deployment behavior monitoring, prompt injection protection, and agent drift issues through a four-layer architecture: Guardrails, Model-as-Judge, human supervision, and circuit breaker mechanisms.
Section 02
Section 03
Original Author and Source
Most AI security guidelines stay at the model layer, while the AI Runtime Security framework focuses on what happens post-deployment: the behavior of AI systems in production environments, how to monitor these behaviors, and how to control things when they go wrong.
This framework is built based on over 20 years of enterprise cybersecurity experience in the regulated financial services sector, specifically targeting runtime security issues like prompt injection, model manipulation, and agent drift.
You cannot fully test an AI system before deployment for three reasons:
So how do we know if AI is operating correctly in production?
The industry is independently converging on the same answer. NVIDIA NeMo, AWS Bedrock, Azure AI, LangChain, Guardrails AI, etc., have all implemented similar patterns:
| Layer | Function | Response Speed | Role |
Section 04
Original Author and Source
Original Author and Source
Core Insight: The Problem with AI Systems Is Not Just Vulnerabilities, But Behavior
Most AI security guidelines stay at the model layer, while the AI Runtime Security framework focuses on what happens post-deployment: the behavior of AI systems in production environments, how to monitor these behaviors, and how to control things when they go wrong.
This framework is built based on over 20 years of enterprise cybersecurity experience in the regulated financial services sector, specifically targeting runtime security issues like prompt injection, model manipulation, and agent drift.
Core Question: Why Is Traditional Testing Insufficient?
You cannot fully test an AI system before deployment for three reasons:
So how do we know if AI is operating correctly in production?
Four-Layer Security Architecture
The industry is independently converging on the same answer. NVIDIA NeMo, AWS Bedrock, Azure AI, LangChain, Guardrails AI, etc., have all implemented similar patterns:
| Layer | Function | Response Speed | Role |
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