Datasets:

Quome
/

policybench

license: apache-2.0
pretty_name: PolicyBench
language:
  - en
tags:
  - clinical-ai
  - llm-security
  - ai-agents
  - healthcare
  - haarf
  - quokkaguard
  - synthetic
size_categories:
  - n<1K
task_categories:
  - other

PolicyBench

Context-aware, auditable policy-as-code authorization for clinical AI agents, with Cedar and Rego behind one trait.

PolicyBench is the benchmark corpus for Policy-as-Code Authorization for Clinical AI Agents: Context-Aware Decisions and a Cedar-vs-Rego Comparison, the quwarden module of the QUOKKAGUARD program. It ships with the quwarden repository, which contains the qfire gateway layer under test, the experiment harness, and the paper.

Semantically equivalent clinical authorization policies in Cedar and Rego, a synthetic PIP attribute store, a HAARF coverage map, and 56 labeled request-to-decision cases (seed 42) covering the happy path, out-of-range dose (confused deputy), no-active-encounter, unpaneled patient, and role edges.

All data are synthetic. No real patient data or protected health information (PHI) is included; clinical content is generated from templates with fixed seeds.

Files

File	Size	Rows
`attrs.json`	184 B
`cases/cases.jsonl`	11 KB	56 rows
`cedar/clinical.cedar`	796 B
`coverage-map.json`	246 B
`rego/clinical.rego`	815 B

Record schemas

cases/cases.jsonl: action, args, attrs, expect, principal, resource

How it was generated

The corpus is produced by the generator in the paper repository and is fully deterministic (fixed seeds), so it can be regenerated byte-for-byte.

# from the quwarden repository root (deterministic seeds)
python3 scripts/005-policy-authz/gen.py --out datasets/005-policy-authz/cases

Intended use

Evaluating the policy enforcement layer of a clinical-agent security gateway (HAARF control C8): A static tool allow-list lets an agent order opioids as long as the role permits it, ignoring dose, encounter, and panel: on PolicyBench, context-blind RBAC over-permits 42.9% of requests.

The experiments that consume it (E-series in the paper) are reproduced from the repository:

git clone https://github.com/quome-cloud/quwarden
cd quwarden
cargo build --release

then follow the Reproduce the experiments section of its README.

Citation

This benchmark was built to evaluate a control of the Healthcare AI Agents Regulatory Framework (HAARF), the source framework for the QUOKKAGUARD program. Please cite both the paper and HAARF:

@unpublished{schwoebel2026quwarden,
  author = {Schwoebel, James},
  title  = {Policy-as-Code Authorization for Clinical AI Agents: Context-Aware Decisions and a Cedar-vs-Rego Comparison},
  note   = {Preprint. Quome, QUOKKAGUARD program (quwarden module)},
  year   = {2026},
  url    = {https://github.com/quome-cloud/quwarden}
}

@unpublished{schwoebel2026haarf,
  author = {Schwoebel, Jim and Frasch, Martin and Spalding, Art and Sewell, Ed and Englert, Phil and Halpert, Ben and Overbay, Collin and Semenec, Ingrida and Shor, Joel},
  title  = {{HAARF}: Healthcare {AI} agents regulatory framework --- a comprehensive security verification standard for autonomous {AI} systems in clinical environments},
  note   = {medRxiv Preprint},
  year   = {2026},
  month  = {April},
  doi    = {10.64898/2026.04.09.26350519},
  url    = {https://www.medrxiv.org/content/10.64898/2026.04.09.26350519v1}
}