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README.md

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----
-license: mit
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-dataset_info:
-  features:
-  - name: Taxonomy
-    dtype: string
-  - name: Scenario
-    dtype: string
-  - name: Risk Description
-    dtype: string
-  - name: Priority
-    dtype: int64
-  - name: Control Name
-    dtype: string
-  - name: Control Activities
-    dtype: string
-  - name: COBIT Objectives
-    dtype: string
-  - name: Attribution
-    dtype: string
-  - name: Related Standards
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 33340
-    num_examples: 100
-  download_size: 18231
-  dataset_size: 33340
----
+---
+---
+license: cc-by-4.0
+task_categories:
+- tabular-classification
+- risk-assessment
+tags:
+- ISO-42001
+- ISO-42005
+- COBIT-2019
+- GRC
+- ai-governance
+- internal-controls
+language:
+- en
+size_categories:
+- n<1K
+---
+
+# AI Risk Scenarios & Control Library
+
+## Dataset Summary
+
+This dataset contains **100 Common AI Risk Scenarios** paired with specific **Control Activities**. It is designed to serve as the operational backbone for an **ISO/IEC 42001 (AI Management System)**.
+
+While ISO 42001 provides the "What" (Requirements), this dataset provides the "How" (Scenarios and Controls), specifically mapping to **ISO/IEC 42005 (AI Risk Management)** impact assessments and **COBIT 2019** objectives.
+
+## Author & Attribution
+
+This library was developed and curated by:
+**Prof. Hernan Huwyler, MBC, CPA**
+*   Academic Director
+*   AI GRC Director
+
+*This dataset reflects a synthesis of global best practices in IT Audit and AI Governance.*
+
+## Dataset Structure
+
+The dataset contains the following fields:
+
+*   **Taxonomy:** The domain of the risk (e.g., *Strategy, Governance, Architecture, Lifecycle*).
+*   **Scenario:** The short name of the risk event.
+*   **Risk description:** A detailed explanation of what goes wrong and the resulting impact (Financial, Reputational, Operational).
+*   **Priority:** Suggested risk rating (1 = High, 3 = Low) to assist in heatmapping.
+*   **Control name:** The title of the mitigation strategy.
+*   **Recommended control activities:** The specific steps, documentation, or technical implementations required to mitigate the risk.
+*   **COBIT 2019 Objectives:** Mapping to the COBIT framework (e.g., *Align, Plan and Organize*).
+
+## Use Cases
+
+### 1. ISO 42001 Implementation (Annex A)
+When implementing the internal controls listed in **ISO 42001 Annex A**, use this dataset to flesh out the specific activities.
+*   *Annex A Reference:* **A.6.1 AI System Life Cycle**.
+*   *Dataset Mapping:* Filter by `Taxonomy = Lifecycle` to find controls for "Hypothesis Testing" and "Model Validation."
+
+### 2. AI Risk Assessment (ISO 42001,ISO 42005, ISO 32894, NIST AI RISK)
+Use the **Risk description** column to populate your Risk Register.
+*   *Scenario:* "Model Overfitting".
+*   *Impact:* "Model performs well on training data but poorly on new data."
+*   *Mitigation:* Implement "Overfitting mitigation" controls defined in this dataset.
+
+### 3. Audit & Assurance
+Internal Auditors can use the **Recommended control activities** as a checklist to verify if the organization's AI governance is effective.
+
+## Example Data
+
+| Taxonomy | Scenario | Priority | Control Name | Control Activities |
+|---|---|---|---|---|
+| **Strategy** | Strategy deficiency | 1 | Enterprise AI strategy | Develop and implement a comprehensive enterprise-wide AI strategy aligning with business objectives. |
+| **Lifecycle** | Data accuracy failures | 1 | Data accuracy verification | Enforce data accuracy verification standards. Document validation, error detection, and correction processes. |
+| **Security** | Vulnerability blindness | 3 | Vulnerability testing | Conduct periodic penetration tests and 'Red-team' reviews. |
+
+## Citation
+If you use this dataset in research or corporate frameworks, please cite:
+> Huwyler, H. (2024). AI Risk Scenarios & Control Library. Hugging Face Datasets.