Granite.Trust Policy Tools: Shareable, Actionable Policies for Generative AI Applications

2026-08-24

AI Summary

为了解决生成式AI应用中内容约束难以实施的问题，提出了可操作策略模式和合成数据生成管道，以实现从模型对齐到运行时监控的全生命周期政策执行。

Abstract

When it comes to safety policies for generative AI, one size does not fit all. Each organization and use case needs to mitigate different risks depending on the application context, regulatory environment, organizational values, and user personas. Yet, existing policy specification approaches are designed for traditional access control and fail to capture the nuances of GenAI application: the enforcement of content-based constraints. We present two contributions to address this gap: (1) the Actionable Policy schema, a YAML-based format for specifying what model responses can and cannot contain. The schema enables exception-based policy governance, proposing exceptions to track policy violations; (2) synthetic data generation pipeline that produces policy-aligned training data for model alignment and testing, and a set of tools to help define the schema and enforce policy. Together, these enable organizations to specify policies once and enforce them throughout the GenAI application lifecycle: from model alignment to runtime monitoring. The Actionable Policy schema, example policies, and tools are available as open source: https://github.com/ibm-granite/granite.trust.policy-tools We welcome new ideas, contributions and feedback.

Problem

• generative AI
• safety policies
• content-based constraints
• policy specification

Innovation

• Actionable Policy schema
• YAML-based format
• synthetic data generation pipeline
• policy-aligned training data