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This release introduces a specialized YAML schema and tooling stack designed specifically for enforcing content-based constraints in generative AI, addressing the limitations of traditional access control models. By enabling exception-based governance and generating synthetic policy-aligned data, these tools facilitate consistent safety enforcement across the entire model lifecycle from alignment to runtime monitoring. The open-source nature of the project lowers barriers for organizations seeking to customize safety policies without relying on vendor lock-in.
Granite.Trust Policy Tools: Shareable, Actionable Policies for Generative AI Applications
arXiv:2608.23870v1 Announce Type: new 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.
Title:Granite.Trust Policy Tools: Shareable, Actionable Policies for Generative AI Applications
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: this https URL We welcome new ideas, contributions and feedback.
Artificial Intelligence (cs.AI)
arXiv:2608.23870 [cs.AI]
(or arXiv:2608.23870v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.23870 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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