Datasets:

murraylovecode
/

enterprise-data-agent-governance

@@ -1,183 +1 @@
----
-annotations_creators:
-- expert-generated
-language:
-- en
-license: cc-by-4.0
-multilinguality:
-- monolingual
-pretty_name: Enterprise Data Agent Governance
-size_categories:
-- n<1K
-source_datasets:
-- original
-tags:
-- enterprise-ai
-- ai-agents
-- governance
-- responsible-ai
-- text-to-sql
-- evaluation
-- security
-- data-governance
-- refusal
-- semantic-layer
-- synthetic-data
-configs:
-- config_name: evaluation_cases
-  data_files:
-  - split: test
-    path: data/evaluation-set.jsonl
-  default: true
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: band
-    dtype: string
-  - name: persona
-    dtype: string
-  - name: question
-    dtype: string
-  - name: required_fields
-    sequence: string
-  - name: expected
-    dtype: string
-  - name: rationale
-    dtype: string
-  splits:
-  - name: test
-    num_examples: 18
----
-# Enterprise Data Agent Governance
-**A synthetic evaluation dataset and machine-readable control framework for testing whether enterprise data agents should answer, request clarification or refuse a question.**
-**Last reviewed:** 15 September 2026 · **Version:** 0.1.0 · **Repository type:** Dataset
-Enterprise Data Agent Governance is an open practitioner framework for deciding when an AI agent may answer a question from enterprise data, when it must request clarification or defer, and what evidence an organization should retain.
-This dataset distribution is intended for Hugging Face’s dataset viewer and evaluation workflows. [GitHub](https://github.com/murraylovecode/enterprise-data-agent-governance) remains canonical for files, schemas, version history and releases. The [public reference site](https://murraylovecode.github.io/enterprise-data-agent-governance/) explains the wider framework; the GitBook implementation guide will be linked at `{{GITBOOK_URL}}` after its final URL is confirmed.
-## Dataset summary
-The test split contains 18 synthetic questions, equally divided across three expected decision bands:
-| Band | Rows | Intended behavior |
-| --- | ---: | --- |
-| `answer` | 6 | Permission, fields and meaning are sufficient to answer. |
-| `needs_definition` | 6 | Data may exist, but an owned definition, threshold or scope is needed. |
-| `refuse` | 6 | Data, authorization, assumptions or an approved action is absent. |
-The questions and personas are synthetic. The repository contains no confidential company data, credentials, customer records, production schemas, query logs, personal information or event-registration data.
-## Contents
-| Path | Purpose |
-| --- | --- |
-| `data/evaluation-set.jsonl` | Viewer-compatible evaluation cases (`test` split) |
-| `data/controls.json` | Sixteen governance controls |
-| `data/mnemiq-evidence.json` | Version-pinned worked-example evidence record |
-| `schemas/evaluation-case.schema.json` | JSON Schema for each evaluation row |
-| `schemas/governance-control.schema.json` | JSON Schema for the control set |
-| `CITATION.cff` | Canonical citation metadata copied from GitHub |
-| `LICENSE-CONTENT` | CC BY 4.0 text and data terms |
-| `LICENSE-CODE` | MIT terms for code |
-## Evaluation fields
-| Field | Meaning |
-| --- | --- |
-| `id` | Stable case identifier: `ANS`, `DEF` or `REF` prefix |
-| `band` | One of `answer`, `needs_definition`, `refuse` |
-| `persona` | Synthetic requester role |
-| `question` | Synthetic enterprise-data question |
-| `required_fields` | Fields needed for the intended decision route; empty where none should be retrieved |
-| `expected` | Expected routing behavior, including the reason for refusal where applicable |
-| `rationale` | Human-readable reason for the expected route |
-`required_fields` is a sequence of strings in every row, including an empty sequence when appropriate, so the viewer receives a stable feature type.
-## Intended use
-Use this dataset to build an organization-specific AI agent evaluation benchmark for governed text-to-SQL and other enterprise AI data access paths. A typical evaluation should:
-1. choose a bounded schema and synthetic or safely controlled data;
-2. map the sample fields to local equivalents;
-3. define personas, grants, certified meanings and expected outcomes before running tests;
-4. capture the response, generated query, policy decision and verification state;
-5. grade the routing decision and, for answered cases, the returned data; and
-6. rerun after material schema, semantic, policy, model, prompt or data changes.
-The set is also suitable for AI agent refusal testing, regression-test design, governance-control workshops and evaluation-harness examples.
-## Recommended metrics
-Answer rate alone is inadequate: it measures coverage, not truth or safety. Report at least:
-- **Answer rate:** attempted answers divided by all cases.
-- **Correct-answer rate:** correct answers divided by answered cases.
-- **Appropriate-refusal rate:** safely declined refusal-required cases divided by refusal cases.
-- **Harmful-answer rate:** unsupported, unauthorized or materially wrong answers divided by all cases.
-- **Clarification accuracy:** correct requests for clarification or correct application of a certified definition divided by definition-dependent cases.
-Publish numerators, denominators, grading rules, dataset version, system configuration and failure examples. Grade returned data where possible rather than SQL-string similarity alone.
-## Inappropriate uses
-Do not use this dataset to:
-- claim universal safety, compliance, security, certification or legal adequacy;
-- rank products without a disclosed, reproducible and like-for-like protocol;
-- authorize access to production data;
-- substitute synthetic results for deployment-specific controls;
-- train a system to infer restricted schema or evade policy; or
-- claim that [Mnemiq](https://github.com/agenticfabriq/mnemiq) passed this benchmark without an actual, reproducible evaluation.
-## Mnemiq evidence record
-Mnemiq is included as a publicly inspectable worked case study. The evidence record is pinned to upstream commit `a08fe859c2ee38eb95f3239ee71a11e8f47b75ba`, reviewed 15 September 2026, and distinguishes `implemented`, `supported`, `implemented_and_evaluated` and `deployment_responsibility` statuses. These are scoped code-and-documentation observations, not a benchmark result, security audit, certification or statement about an unknown deployment.
-## Dataset creation and validation
-The evaluation cases and controls are copied from the canonical GitHub repository at framework version 0.1.0. The Mnemiq JSON record is a faithful JSON conversion of the canonical YAML evidence record. Preparation checks include:
-- the repository’s standard-library validator;
-- JSON and line-by-line JSONL parsing;
-- JSON Schema validation where a validator is available;
-- stable feature-type and row-count checks for dataset-viewer compatibility;
-- privacy and prohibited-data scans;
-- link, disclosure, license and unsupported-claim checks.
-See `VALIDATION.md` in this upload bundle for recorded results.
-## Limitations
-The 18 cases are a starter set, not a representative sample of every enterprise schema, sector, jurisdiction or threat. Synthetic questions do not measure the correctness of a live database result by themselves. Local definitions, access policies, data quality, source behavior and operating controls determine deployment outcomes. Passing one band or control does not compensate for a failure elsewhere.
-The framework is not legal, compliance or security advice. Organizations must adapt it to their own risk, data, jurisdiction and operating environment.
-## Disclosures and maintenance
-This project is independently maintained by Murray Newlands as a personal practitioner resource. It is not official product documentation for Mnemiq or [Agentic Fabriq](https://www.agenticfabriq.com/) and does not constitute independent certification or validation of any vendor. Agentic Fabriq develops and maintains Mnemiq. Murray Newlands advises Agentic Fabriq.
-The approved dataset identifier is `murraylovecode/enterprise-data-agent-governance`. Murray Newlands independently maintains the resource under the `murraylovecode` identity. It is not an Open Future Forum research report or official vendor documentation.
-## Contributing
-Propose additional cases through the [canonical GitHub repository](https://github.com/murraylovecode/enterprise-data-agent-governance/blob/main/CONTRIBUTING.md). Each case should use synthetic content, identify its decision band, expected route and rationale, and avoid credentials, confidential schemas, customer records and personal information. Product mappings must cite a version or commit and disclose relevant relationships.
-## License and citation
-Text and data are licensed under [CC BY 4.0](LICENSE-CONTENT). Code is licensed under the [MIT License](LICENSE-CODE).
-Suggested citation, derived from the authoritative `CITATION.cff`:
-> Newlands, Murray. *Enterprise Data Agent Governance*. Version 0.1.0, 15 September 2026. https://github.com/murraylovecode/enterprise-data-agent-governance
-## Links
-- Canonical GitHub: https://github.com/murraylovecode/enterprise-data-agent-governance
-- Public reference site: https://murraylovecode.github.io/enterprise-data-agent-governance/
-- GitBook: `{{GITBOOK_URL}}` (replace only after confirmation)


1	+ Link the public GitBook implementation guide