Can AI Generate Authorization Policy Safely?

AI-generated authorization policies present a complex challenge, as they can appear coherent yet be fundamentally incorrect due to semantic, rather than syntactic, discrepancies. This issue is exacerbated in AI-heavy ecosystems where policy generation is intertwined with planning, tool use, and delegation, potentially leading to inappropriate access under realistic conditions. The key to safe AI-generated policy lies in explicitly modeling intent, human-reviewed boundaries, and verifier-constrained synthesis, ensuring live context guides runtime decisions. Practical approaches involve decomposing requirements into testable intent atoms, validating schemas and boundaries, and employing verifier-guided policy synthesis, as demonstrated by AutoCedar. Runtime enforcement requires a Protocol Data Processing (PDP) layer, such as Permit, to evaluate dynamic factors like agent identity and tool arguments, ensuring that decisions made in production align with the verified policy model. Comprehensive testing, staged deployments, and robust audits are essential to maintain policy integrity and compliance, making AI-assisted policy engineering a collaborative and accountable endeavor.

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