Technology Category
Philosophy and Ethics of AI: Safety, Robustness & TrustworthinessNatural Language Processing: Safety and RobustnessHumans and AI: Human-Aware Planning and Behavior Prediction
The AI Agent Code of Conduct: Automated Guardrail Policy-as-Prompt Synthesis
🤖 AI Summary
Autonomous AI agents deployed in industrial settings face significant challenges in ensuring runtime safety and regulatory compliance. Method: This paper introduces the “Policy-as-Prompt” paradigm, which automatically transforms unstructured design documentation into verifiable, auditable, real-time safety guardrails. Leveraging large language models, the approach parses technical documents to extract security policy semantics, enforces least-privilege constraints, constructs structured policy trees, and compiles them into lightweight, prompt-driven classifiers for low-overhead behavioral auditing. Results: Experiments demonstrate scalability and auditability across diverse industrial scenarios, effectively bridging the gap between policy formulation and enforcement. The key contribution is the first end-to-end automated translation of natural-language security policies into formally verifiable, runtime-enforceable guardrails—establishing a novel AI governance framework that jointly ensures security, regulatory compliance, and interpretability.
Technology Category
Application Category
📝 Abstract
As autonomous AI agents are increasingly deployed in industry, it is essential to safeguard them. We introduce a novel framework that automates the translation of unstructured design documents into verifiable, real-time guardrails. We introduce "Policy as Prompt," a new approach that uses Large Language Models (LLMs) to interpret and enforce natural language policies by applying contextual understanding and the principle of least privilege. Our system first ingests technical artifacts to construct a verifiable policy tree, which is then compiled into lightweight, prompt-based classifiers that audit agent behavior at runtime. We validate our approach across diverse applications, demonstrating a scalable and auditable pipeline that bridges the critical policy-to-practice gap, paving the way for verifiably safer and more regulatable AI.
Problem
Research questions and friction points this paper is trying to address.
Automating translation of design documents into verifiable guardrails
Using LLMs to interpret and enforce natural language policies
Bridging the policy-to-practice gap for safer autonomous AI agents
Innovation
Methods, ideas, or system contributions that make the work stand out.
Automates translation of documents into verifiable guardrails
Uses LLMs to interpret natural language policies
Compiles policies into prompt-based runtime classifiers
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