| --- |
| pretty_name: "TenantForge sample tenant: Northmere Health Partners (healthcare provider)" |
| license: other |
| license_name: tenantforge-evaluation |
| license_link: LICENSE |
| extra_gated_prompt: "TenantForge sample tenant: evaluation licence. By requesting access you agree to use this synthetic dataset only for internal evaluation, testing and benchmarking; not to redistribute it; not to represent any of its content as real or use its synthetic identities to create or seed real accounts; and not to use it to train a product or service that is sold without a commercial licence from Tidewater Labs (legal@tidewaterlabs.tech). Full text in LICENSE." |
| language: |
| - en |
| tags: |
| - security |
| - dlp |
| - dspm |
| - pii |
| - synthetic |
| - benchmark |
| - data-loss-prevention |
| - agent-evaluation |
| - phi |
| - hipaa |
| - healthcare |
| - pci |
| - payment-cards |
| - vcdpa |
| size_categories: |
| - n<1K |
| task_categories: |
| - token-classification |
| - text-classification |
| configs: |
| - config_name: default |
| data_files: rows.jsonl |
| --- |
| |
| # TenantForge sample tenant: Northmere Health Partners |
|
|
| A synthetic **healthcare provider** enterprise tenant (HIPAA, PCI DSS, VCDPA) for testing DLP and DSPM tools and |
| evaluating AI agents on sensitive data: 191 files (Word, Excel, CSV, PDF, scanned PDF, PNG, |
| text, Markdown, .eml, mbox, chat exports, code, bucket listings, zips), identities, permissions, a year of |
| activity logs, and an exact label for every one of 894 sensitive values plus 317 enumerated |
| decoys. **No real personal data by construction. Same inputs rebuild byte for byte on any operating system.** |
|
|
| - `tenantforge-sample.zip`: the full tenant. Unzip and run `python validate.py .` to re-check every label against the content. |
| - `rows.jsonl`: 172 text-bearing artifacts flattened for the dataset viewer (text plus findings with locators). Image-only |
| scans and zip members are only in the archive. |
| - `labels.jsonl`, `artifacts.jsonl`, `manifest.json`: the label set, artifact inventory and provenance, as shipped. |
| - `detector.json`: the baseline recall numbers behind the leaderboard below. |
|
|
| Tenant id `tf_4ac1bd2ede4f`, content hash `2232bd6ee5a40ad8e02a5a16e7d8509d55ac5a1a4e9e40d2a8231c302cc3bc8e`, generator tenantforge 0.1.2. |
|
|
| Other industries in the free sample collection: https://huggingface.co/collections/jeffreyphall/tenantforge-sample-tenants-6ae8f9824aead1f72f5a181f |
|
|
| Licence: evaluation only (see LICENSE). Standard (5,000) and large (50,000) tenants in sixteen industries and |
| regimes are available under a commercial licence: https://tidewaterlabs.tech/tenantforge/ |
|
|
| ## Detector leaderboard |
|
|
| Recall of two free baselines over every labelled true value in the text-bearing artifacts (894 values; 40 image-only and 83 zip-member values excluded because these baselines do not open them). Format regexes are the plain patterns in `tenantforge/detector.py`; Presidio is Microsoft Presidio 2.2.366 with spaCy `en_core_web_sm`, score threshold 0.4. Raw numbers in `detector.json`. |
|
|
| | data type | labelled | format regex | Presidio | |
| |---|---:|---:|---:| |
| | ACCESS_TOKEN | 5 | 1.00 | 0.00 | |
| | API_KEY | 3 | 0.33 | 0.00 | |
| | BANK_ACCOUNT | 5 | 0.00 | 0.00 | |
| | CARD_EXPIRY | 6 | 0.00 | 0.00 | |
| | CLOUD_CREDENTIAL | 3 | 0.00 | 0.00 | |
| | CONTRACT_TERM | 14 | 0.00 | 0.00 | |
| | DB_CONNECTION_STRING | 8 | 1.00 | 0.00 | |
| | DOB | 74 | 0.00 | 0.82 | |
| | FINANCIAL_PRE_RELEASE | 3 | 0.00 | 0.00 | |
| | HOME_ADDRESS | 20 | 0.00 | 0.50 | |
| | LEGAL_PRIVILEGED | 13 | 0.00 | 0.00 | |
| | PAN | 26 | 0.81 | 0.65 | |
| | PAN_LAST4 | 24 | 0.00 | 0.00 | |
| | PASSPORT | 1 | 0.00 | 0.00 | |
| | PASSWORD | 5 | 0.00 | 0.00 | |
| | PERFORMANCE_REVIEW | 7 | 0.00 | 0.00 | |
| | PERSONAL_EMAIL | 6 | 0.83 | 0.83 | |
| | PERSONAL_PHONE | 13 | 0.69 | 0.69 | |
| | PERSON_NAME | 291 | 0.00 | 0.78 | |
| | PHI_CLAIM | 55 | 0.00 | 0.00 | |
| | PHI_DX | 88 | 0.00 | 0.00 | |
| | PHI_INSURANCE_ID | 10 | 0.00 | 0.00 | |
| | PHI_MRN | 138 | 0.73 | 0.00 | |
| | PHI_RX | 4 | 0.00 | 0.00 | |
| | PRIVATE_KEY | 3 | 1.00 | 0.00 | |
| | ROUTING_NUMBER | 4 | 0.75 | 0.00 | |
| | SALARY | 12 | 0.00 | 0.00 | |
| | SOURCE_CODE | 12 | 0.00 | 0.00 | |
| | SSN | 32 | 0.62 | 0.38 | |
| | SSN_LAST4 | 6 | 0.00 | 0.00 | |
| | TAX_ID | 3 | 0.00 | 0.00 | |
| | **all (weighted)** | 894 | **0.20** | **0.38** | |
|
|
| Decoys fired on: format regex 149, Presidio 126. A decoy is an enumerated value that looks sensitive and is not (test card numbers, order numbers shaped like SSNs, lorem keys); every fire is a false positive the label set lets you count exactly. |
|
|
| **Add your tool.** Run it over the unzipped tenant, score it with `python validate.py . --compare your-hits.jsonl` (one `{path, value, data_type}` per line) or your own matcher against `labels.jsonl`, and open a discussion on this repo with the per-type recall and decoy-fire counts. Rows from DLP and DSPM vendors are listed with the product and version named. |
|
|
|
|
| ## Purpose |
|
|
| A synthetic enterprise tenant (files, mail, chat exports, code snippets, cloud |
| bucket listings, identities, permissions and activity logs) with exact |
| ground-truth labels, for testing and benchmarking data-loss-prevention (DLP) and |
| data-security-posture (DSPM) tools and for evaluating AI agents that must handle |
| sensitive data correctly. |
|
|
| Company: **Northmere Health Partners** (healthcare_provider), 1200 staff, |
| regimes: HIPAA, PCI_DSS, VCDPA. Timeline 2025-01-06 to |
| 2025-12-19, evaluated as of 2026-01-15. |
|
|
| ## Contents |
|
|
| | item | count | |
| |---|---| |
| | artifacts (top level) | 191 | |
| | artifacts incl. attachments and archive members | 215 | |
| | true findings (labels.jsonl, is_decoy=false) | 894 | |
| | decoys (labels.jsonl, is_decoy=true) | 317 | |
| | identities / groups | 1229 / 30 | |
| | folders / buckets | 84 / 4 | |
| | log events / planted scenarios | 8690 / 6 | |
|
|
| | format | artifacts | |
| |---|---| |
| | bucket_listing | 4 | |
| | chat_json | 3 | |
| | code | 16 | |
| | csv | 11 | |
| | docx | 63 | |
| | eml | 8 | |
| | mbox | 1 | |
| | md | 11 | |
| | pdf | 33 | |
| | pdf_scanned | 12 | |
| | png | 6 | |
| | txt | 12 | |
| | xlsx | 28 | |
| | zip | 7 | |
| |
| ## Files |
| |
| ``` |
| files/, buckets/, mail/, chat/, repos/ the artifacts |
| logs/file_access/*.jsonl access events by month |
| logs/sharing_changes.jsonl, downloads.jsonl |
| logs/scenarios.jsonl ground truth for planted exfiltration-like sequences |
| identities.jsonl groups.jsonl acl.jsonl buckets.jsonl subjects.jsonl |
| artifacts.jsonl one row per artifact (exposure is authoritative here) |
| labels.jsonl one row per labeled occurrence (schema: finding.schema.json) |
| text_parts.jsonl extracted PDF text the offsets refer to |
| manifest.json provenance and file hashes |
| validate.py re-checks every label against the content |
| ``` |
| |
| ## Label schema |
| |
| See `schemas/finding.schema.json`. Each finding has one or more locators |
| (`char_span`, `cell_ref`, `json_pointer`, `image_region`, `archive_member`, |
| `email_attachment`), a data type, sensitivity tier, applicable regulations for |
| this tenant, the exposure context, decoy flag and hard-case tags. Decoys are |
| values that look sensitive and are not; they are enumerated so false positives |
| can be scored. |
| |
| ## Generation method |
| |
| Everything is a pure function of (generator version `0.1.2`, |
| config hash `76d43d80ee45b5c3...`, seed `20260131`). The structure |
| layer (identities, share tree, permissions, which values go where) is |
| deterministic Python. Values are injected by that layer, so each label records |
| the exact location rather than being inferred from text afterwards. Prose comes |
| from rule-based templates. Local model used for text: `qwen3:14b` revision `ollama:bdbd181c33f2ed1b31c972991882db3cf4d192569092138a7d29e973cd9debe8`, licence Apache-2.0, source `hf:Qwen/Qwen3-14B`. |
| No text from any commercial LLM API is included. |
| |
| Content hash (all files except manifest.json): `de6f7d73510a53a562defe5daed4467335ce87204f796b63ef8bcf58e293c197`. |
| Dependency versions are recorded in manifest.json; rebuilding with the same |
| versions reproduces every byte. |
| |
| ## Safety construction (no real personal data) |
| |
| - SSNs use area 900-999 or 000 with group digits outside the ITIN ranges, so no |
| value can be an issued SSN or ITIN. Detectors that validate SSN area numbers |
| will therefore reject every true SSN here; score them in format-only mode. |
| - Payment card numbers are the card networks' and processors' published test |
| numbers only (37 distinct values; expect repetition). Decoys flip one digit so |
| Luhn fails. |
| - Phone numbers are 555-0100 to 555-0199. Email addresses and domains use |
| example.com/org/net or reserved `.example`/`.test` names. Routing numbers use |
| unassigned ABA prefixes 13-20 with a valid check digit. EINs use the unassigned |
| `00-` prefix. IBANs are published examples. |
| - Names are seeded Faker output screened against a public-figure list. They are |
| common names and will coincide with private individuals who share them; that |
| is unavoidable and is not personal data about those individuals. |
| - Addresses are synthetic street names with 5-digit house numbers and random |
| ZIP codes that do not correspond to the city. |
| - Driver's licence and passport numbers are format-plausible only and are |
| disabled by default; do not rely on them being unissued. |
| - Private keys are PEM-shaped random data, not valid keys. |
| |
| ## Known limitations |
| |
| - Prose is templated and repetitive; it is not a realism benchmark for text. |
| - The scanned-document and screenshot artifacts are rendered, not photographed. |
| - Activity logs are statistically shaped, not replayed from a real system. |
| - Only `en_US` names and US geography are generated in this version. |
| - Pre-release financial figures, contract terms and similar business-confidential |
| content are invented; nothing refers to a real company. |
| |
| ## Intended uses |
| |
| Detector precision/recall measurement, DSPM exposure-finding evaluation, |
| AI-agent red-teaming on sensitive-data handling, training data for |
| classification models where synthetic provenance is acceptable, demos. |
| |
| ## Prohibited uses |
| |
| Representing any content here as real; using the synthetic identities to |
| impersonate or to seed real-world accounts; redistribution outside the terms in |
| LICENSE. |
| |
| ## Licence |
| |
| This tenant ships under the `evaluation` licence text in |
| LICENSE (evaluation: free sample, internal evaluation and benchmarking only; commercial: |
| per-organisation internal use including training; placeholder: no rights granted beyond |
| evaluation on the recipient's own systems). |
| |
