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policybench

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Languages:
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Tags:
clinical-ai
llm-security
ai-agents
healthcare
haarf
quokkaguard
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
action: string
args: struct<dose_mg: int64, drug: string>
  child 0, dose_mg: int64
  child 1, drug: string
attrs: struct<active_encounter: bool, max_dose_mg: int64, paneled: bool>
  child 0, active_encounter: bool
  child 1, max_dose_mg: int64
  child 2, paneled: bool
expect: string
principal: string
resource: string
prescriber|patient-2: struct<active_encounter: bool, paneled: bool, max_dose_mg: int64>
  child 0, active_encounter: bool
  child 1, paneled: bool
  child 2, max_dose_mg: int64
prescriber|patient-1: struct<active_encounter: bool, paneled: bool, max_dose_mg: int64>
  child 0, active_encounter: bool
  child 1, paneled: bool
  child 2, max_dose_mg: int64
to
{'prescriber|patient-1': {'active_encounter': Value('bool'), 'paneled': Value('bool'), 'max_dose_mg': Value('int64')}, 'prescriber|patient-2': {'active_encounter': Value('bool'), 'paneled': Value('bool'), 'max_dose_mg': Value('int64')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              action: string
              args: struct<dose_mg: int64, drug: string>
                child 0, dose_mg: int64
                child 1, drug: string
              attrs: struct<active_encounter: bool, max_dose_mg: int64, paneled: bool>
                child 0, active_encounter: bool
                child 1, max_dose_mg: int64
                child 2, paneled: bool
              expect: string
              principal: string
              resource: string
              prescriber|patient-2: struct<active_encounter: bool, paneled: bool, max_dose_mg: int64>
                child 0, active_encounter: bool
                child 1, paneled: bool
                child 2, max_dose_mg: int64
              prescriber|patient-1: struct<active_encounter: bool, paneled: bool, max_dose_mg: int64>
                child 0, active_encounter: bool
                child 1, paneled: bool
                child 2, max_dose_mg: int64
              to
              {'prescriber|patient-1': {'active_encounter': Value('bool'), 'paneled': Value('bool'), 'max_dose_mg': Value('int64')}, 'prescriber|patient-2': {'active_encounter': Value('bool'), 'paneled': Value('bool'), 'max_dose_mg': Value('int64')}}
              because column names don't match

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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}
}

License

Apache License 2.0. Copyright (c) 2026 Quome, Inc.
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