| --- |
| 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`](https://github.com/quome-cloud/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. |
|
|
| ```bash |
| # 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: |
|
|
| ```bash |
| 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: |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
| ## License |
|
|
| Apache License 2.0. Copyright (c) 2026 Quome, Inc. |
|
|
