README.md

---
license: cc-by-4.0
task_categories:
- question-answering
- text-generation
language:
- en
tags:
- cybersecurity
- compliance
- grc
- governance
- risk
- nist
- cis-controls
- cloud-security
size_categories:
- 1K<n<10K
dataset_info:
- config_name: alpaca
  features:
  - name: instruction
    dtype: string
  - name: input
    dtype: string
  - name: output
    dtype: string
  - name: source
    dtype: string
  - name: id
    dtype: string
  splits:
  - name: train
    num_bytes: 2801898
    num_examples: 2154
  download_size: 836652
  dataset_size: 2801898
- config_name: chatml
  features:
  - name: messages
    list:
    - name: role
      dtype: string
    - name: content
      dtype: string
  - name: source
    dtype: string
  - name: id
    dtype: string
  splits:
  - name: train
    num_bytes: 3225039
    num_examples: 2154
  download_size: 851148
  dataset_size: 3225039
configs:
- config_name: alpaca
  data_files:
  - split: train
    path: alpaca/train-*
- config_name: chatml
  data_files:
  - split: train
    path: chatml/train-*
---

# GRC Security Frameworks Dataset

A comprehensive dataset for training AI models on Governance, Risk, and Compliance (GRC) frameworks and cybersecurity standards.

## Dataset Overview

This dataset contains **3,225 high-quality training examples** covering major security and compliance frameworks. It's designed for fine-tuning large language models to become expert GRC assistants.

### Covered Frameworks

- **CIS Controls v8.1.2** - 153 safeguards across 18 control families
- **Cloud Controls Matrix (CCM) v4.0.12** - 197 cloud security controls
- **NIST SP 800-53 Rev 5** - 200 security and privacy controls
- **NIST Cybersecurity Framework v2.0** - 22 subcategories across 6 functions
- **NIST AI Risk Management Framework v1.0** - 72 AI governance actions

## Dataset Structure

### Configurations

The dataset is available in two formats:

#### 1. **Alpaca Format** (`alpaca`)
Standard instruction-tuning format with three fields:

```json
{
  "instruction": "What is CIS Control 1.1?",
  "input": "Provide details about this safeguard.",
  "output": "CIS Control 1.1: Establish and Maintain Detailed Enterprise Asset Inventory...",
  "metadata": {
    "source_framework": "CIS Controls v8.1.2",
    "control_id": "1.1",
    "dataset_type": "unified_controls"
  }
}
```

#### 2. **ChatML Format** (`chatml`)
Conversational format with role-based messages:

```json
{
  "messages": [
    {
      "role": "system",
      "content": "You are a GRC compliance expert..."
    },
    {
      "role": "user",
      "content": "What is CIS Control 1.1?"
    },
    {
      "role": "assistant",
      "content": "CIS Control 1.1: Establish and Maintain Detailed Enterprise Asset Inventory..."
    }
  ],
  "metadata": {
    "source_framework": "CIS Controls v8.1.2",
    "control_id": "1.1",
    "dataset_type": "unified_controls"
  }
}
```

### Dataset Splits

- **Alpaca**: 3,225 examples
  - Unified Controls: 797 examples
  - Framework Mappings: 2,167 examples
  - Assessment Questions: 261 examples

- **ChatML**: 3,072 examples
  - Unified Controls: 644 examples
  - Framework Mappings: 2,167 examples
  - Assessment Questions: 261 examples

## Usage

### Load with Hugging Face Datasets

```python
from datasets import load_dataset

# Load Alpaca format
dataset = load_dataset("Zeezhu/grc-security-frameworks", "alpaca")

# Load ChatML format
dataset = load_dataset("Zeezhu/grc-security-frameworks", "chatml")

# Split into train/test
dataset = dataset['train'].train_test_split(test_size=0.1, seed=42)
train_data = dataset['train']
test_data = dataset['test']
```

### Example Training Use Case

This dataset is ideal for:
- Fine-tuning models for GRC advisory chatbots
- Training compliance automation systems
- Building security framework mapping tools
- Creating assessment question generators
- Developing control implementation assistants

## Dataset Creation

### Source Data

Original data extracted from official framework publications:
- CIS Controls v8.1.2 (JSON)
- CSA Cloud Controls Matrix v4.0.12 (JSON)
- NIST SP 800-53 Rev 5 (OSCAL JSON)
- NIST Cybersecurity Framework v2.0 (JSON)
- NIST AI RMF Playbook v1.0 (JSON)

### Processing Pipeline

1. **Extraction**: Parsed official JSON/OSCAL files
2. **Normalization**: Unified schema across frameworks
3. **Augmentation**: Generated Q&A pairs and mappings
4. **Validation**: Quality checks and format verification
5. **Formatting**: Converted to Alpaca and ChatML formats

### Quality Assurance

- ✅ 100% format validation
- ✅ No duplicates across datasets
- ✅ Consistent metadata tagging
- ✅ Source attribution for all examples
- ✅ Manual spot-checks of content accuracy

## Content Categories

### 1. Unified Controls (644-797 examples)
Individual control explanations with:
- Control ID and title
- Full description
- Implementation guidance
- Asset type relevance
- Security function mapping

### 2. Framework Mappings (2,167 examples)
Cross-framework relationships showing:
- How controls map between frameworks
- Related controls across standards
- Equivalent requirements
- Compliance alignment

### 3. Assessment Questions (261 examples)
Interview-style questions for:
- Control implementation verification
- Compliance gap analysis
- Audit preparation
- Risk assessment

## Limitations

- Dataset reflects framework versions as of generation date
- Does not include proprietary or restricted frameworks
- Focus on technical controls; limited governance/policy content
- English language only
- May not reflect latest framework updates after 2024

## Ethical Considerations

- **Intended Use**: Educational, compliance automation, GRC advisory systems
- **Misuse Risks**: Should not replace professional security audits or legal compliance advice
- **Accuracy**: While sourced from official frameworks, always verify critical compliance decisions
- **Bias**: Reflects cybersecurity industry standards and may not cover all global regulations

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{grc_security_frameworks_2025,
  title={GRC Security Frameworks Dataset},
  author={Zeezhu},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/Zeezhu/grc-security-frameworks}
}
```

## License

This dataset is released under **CC BY 4.0** (Creative Commons Attribution 4.0 International).

You are free to:
- **Share** — copy and redistribute the material
- **Adapt** — remix, transform, and build upon the material

Under the following terms:
- **Attribution** — You must give appropriate credit

### Framework Licenses

Source frameworks retain their original licenses:
- CIS Controls: CIS Terms of Use
- NIST publications: Public domain (U.S. Government work)
- CSA CCM: Creative Commons Attribution 4.0

## Contact

For questions, issues, or contributions:
- **Hugging Face**: [@Zeezhu](https://huggingface.co/Zeezhu)
- **Dataset Repository**: [grc-security-frameworks](https://huggingface.co/datasets/Zeezhu/grc-security-frameworks)

## Version History

- **v1.0** (2025): Initial release
  - 3,225 Alpaca examples
  - 3,072 ChatML examples
  - 5 major frameworks covered
  - 644 unified controls
  - 2,167 framework mappings
  - 261 assessment questions

## Acknowledgments

Special thanks to:
- Center for Internet Security (CIS) for CIS Controls
- Cloud Security Alliance (CSA) for CCM
- National Institute of Standards and Technology (NIST) for SP 800-53, CSF, and AI RMF
- The open-source compliance community