---
language:
- en
license: apache-2.0
tags:
- nist-csf
- grc
- cybersecurity
- compliance
- policy
- qna
task_categories:
- text-generation
pretty_name: GRC NIST CSF 2.0 QA Dataset (v1)
configs:
- config_name: alpaca
  data_files: nist_csf2_qa_alpaca.jsonl
- config_name: chatml
  data_files: nist_csf2_qa_chat.jsonl
---

# Dataset Card for GRC NIST CSF 2.0 QA Dataset (v1)

This dataset contains **question–answer and dialogue pairs** based on the **NIST Cybersecurity Framework (CSF) 2.0**.  
It is intended for training large language models to provide **Governance, Risk, and Compliance (GRC)** guidance.

---

## Dataset Details

### Dataset Description

- **Curated by:** Independent GRCora Project (educational initiative)  
- **Funded by [optional]:** None  
- **Shared by [optional]:** zeezhu (Hugging Face username)  
- **Language(s) (NLP):** English  
- **License:** Apache-2.0 (permissive, allows reuse with attribution)  

### Dataset Sources

- **Repository:** https://huggingface.co/datasets/zeezhu/grc-nist-csf2-qa-v1  
- **Paper [optional]:** NIST CSF 2.0 (official source: https://www.nist.gov/cyberframework)  
- **Demo [optional]:** None yet  

---

## Uses

### Direct Use
- Fine-tuning instruction-following models (e.g., LLaMA, Mistral, Qwen).  
- Training chat assistants for cybersecurity and compliance.  
- Educational tools for NGOs, SMEs, and consultants who need simplified NIST CSF guidance.  

### Out-of-Scope Use
- **Not** a replacement for legal, regulatory, or compliance advice.  
- **Not** suitable as the only source for enterprise risk assessments.  
- Should not be used for critical decision-making without human review.  

---

## Dataset Structure

- **alpaca.jsonl**: Instruction–input–output triples.  
  - Fields:  
    - `instruction` (string)  
    - `input` (string, optional)  
    - `output` (string)  

- **chatml.jsonl**: Chat-formatted dialogues.  
  - Fields:  
    - `messages`: list of objects with `role` (`system`, `user`, `assistant`) and `content` (string).  

- **Splits**: Current version contains a single combined dataset (~3,300 records). Users may create `train/validation/test` splits using random sampling or Unsloth utilities.

---

## Dataset Creation

### Curation Rationale
Created to provide **structured Q&A data** around the NIST CSF 2.0 framework, enabling AI models to learn compliance concepts in an approachable format.

### Source Data

#### Data Collection and Processing
- Extracted from NIST CSF 2.0 JSON exports.  
- Reformatted into Alpaca and ChatML formats for model fine-tuning.  
- Quality checks: ensured valid JSONL (UTF-8), removed empty or duplicate entries.  

#### Who are the source data producers?
- Primary source: NIST (official CSF 2.0 framework text).  
- Reformatted and curated by the GRCora Project team.  

### Annotations
- No manual human annotations; transformation was automated.  

#### Personal and Sensitive Information
- Dataset **does not contain personal, sensitive, or private data**.  
- Content is limited to framework descriptions and compliance Q&A.  

---

## Bias, Risks, and Limitations

- **Bias**: Only covers NIST CSF 2.0; does not reflect other frameworks equally (e.g., ISO 27001, SOC 2).  
- **Risk**: Users may mistake generated answers as authoritative compliance advice.  
- **Limitations**: Simplified responses may not capture full legal/regulatory complexity.  

### Recommendations
- Use as **training/evaluation data**, not as a compliance checklist.  
- Pair with domain expert review for production use.  

---

## Citation

**BibTeX:**
```bibtex
@misc{grc_nist_csf2_qa,
  title        = {GRC NIST CSF 2.0 QA Dataset (v1)},
  author       = {A.O.A},
  year         = {2025},
  howpublished = {Hugging Face Datasets},
  url          = {https://huggingface.co/datasets/zeezhu/grc-nist-csf2-qa-v1}
}
APA:
A.O.A. (2025). GRC NIST CSF 2.0 QA Dataset (v1) [Dataset]. Hugging Face. https://huggingface.co/datasets/zeezhu/grc-nist-csf2-qa-v1

Glossary

NIST CSF 2.0: National Institute of Standards and Technology Cybersecurity Framework, version 2.0.

GRC: Governance, Risk, and Compliance.

Alpaca format: Instruction–input–output JSONL structure used in fine-tuning.

ChatML: Chat-format schema for conversational AI training.

More Information

NIST CSF 2.0 official page: https://www.nist.gov/cyberframework

Dataset curated as part of GRCora (AI-driven GRC SaaS project).

Dataset Card Authors

A.O.A (curator, dataset creator)

Dataset Card Contact

Hugging Face: https://huggingface.co/zeezhu