Datasets:

ethanolivertroy
/

cmmc-training-comprehensive

license: cc0-1.0
task_categories:
  - text-generation
  - question-answering
language:
  - en
size_categories:
  - 10K<n<100K
tags:
  - cmmc
  - nist
  - cybersecurity
  - compliance
  - security-controls
  - SP-800-171
pretty_name: CMMC Training Dataset - Comprehensive Variant

CMMC Training Dataset - Comprehensive Variant

Dataset Description

This is the Comprehensive variant of the CMMC (Cybersecurity Maturity Model Certification) training dataset, containing 11,279 high-quality training examples from the complete NIST CMMC publication library.

Dataset Characteristics

Total Examples: 11,279 (9,023 train / 2,256 validation)
Source Documents: 381 NIST publications
CMMC Levels Covered: Level 1, Level 2, Level 3
CMMC Domains: All 17 domains
Format: JSONL with chat-formatted messages
Embeddings: 1536-dimensional vectors (OpenAI text-embedding-3-small)
License: Public Domain (NIST documents are US Government works)

What Makes This "Comprehensive"?

The Comprehensive variant is the most complete CMMC training dataset available, including virtually every NIST publication relevant to CMMC compliance:

381 source documents from the NIST CSRC library
11,279 training examples covering all aspects of CMMC
Maximum context and coverage for exhaustive knowledge
Research-grade completeness for academic and enterprise use

Document Categories

Core Foundation (14 documents):

NIST SP 800-171 R3, 800-172 R3, 800-53 R5
Assessment procedures and implementation guides

Security Controls & Implementation (~120 documents):

Detailed guides for all 17 CMMC domains
Implementation examples and case studies
Assessment methodologies

Specialized Topics (~247 documents):

Cloud security (800-series)
IoT and mobile security
Supply chain risk management
Incident response and forensics
Cryptography and PKI
Privacy engineering
Security automation
Continuous monitoring
Vulnerability management
And much more...

CMMC Level Distribution

All Levels:         10,430 examples (92.5%)
Level 1 (Foundational): 392 examples (3.5%)
Level 2 (Advanced): 309 examples (2.7%)
Level 3 (Advanced):   148 examples (1.3%)

CMMC Domain Distribution

All 17 CMMC domains are comprehensively covered:

Access Control (AC): 2,042 examples
Awareness and Training (AT): 2,043 examples
Audit and Accountability (AU): 2,042 examples
Configuration Management (CM): 1,922 examples
Identification and Authentication (IA): 2,043 examples
Incident Response (IR): 2,042 examples
Maintenance (MA): 2,043 examples
Media Protection (MP): 2,043 examples
Personnel Security (PS): 2,043 examples
Physical Protection (PE): 2,043 examples
Planning (PL): 2,042 examples
Risk Assessment (RA): 2,043 examples
Security Assessment (CA): 2,043 examples
Supply Chain Risk Management (SR): 1,947 examples
System and Communications Protection (SC): 2,042 examples
System and Information Integrity (SI): 2,042 examples
System and Services Acquisition (SA): 2,042 examples

Note: Domain counts represent the number of examples tagged with each domain. Since examples can be tagged with multiple domains, the sum of domain counts exceeds the total number of examples (11,279).

Average: ~2,014 examples per domain (well-balanced)

Source Documents (381 total)

The Comprehensive variant includes:

FIPS Publications (~15 documents):

Cryptographic standards
Hashing and encryption algorithms
PKI and digital signatures

SP 800-Series Security Guides (~340 documents):

Core compliance (171, 172, 53)
Implementation guides
Risk management
Security controls
Assessment procedures
Specialized topics

Interagency Reports (IR) (~26 documents):

Research findings
Technical analysis
Emerging threats
Implementation case studies

This represents virtually the entire NIST CMMC-relevant catalog available as of 2025.

Dataset Structure

JSONL Training Files

Each example follows the chat format:

{
  "messages": [
    {
      "role": "system",
      "content": "You are a cybersecurity expert specializing in CMMC..."
    },
    {
      "role": "user",
      "content": "What are the cryptographic requirements for CMMC Level 3?"
    },
    {
      "role": "assistant",
      "content": "According to NIST SP 800-172 R3 and FIPS 140-3..."
    }
  ],
  "metadata": {
    "source": "NIST SP 800-172 R3",
    "cmmc_level": "3",
    "cmmc_domain": "System and Communications Protection",
    "type": "cmmc_requirement"
  }
}

Vector Embeddings

Pre-computed embeddings using OpenAI's text-embedding-3-small model:

Format: Parquet files with 1536-dimensional vectors
Files: embeddings_train.parquet, embeddings_valid.parquet
Size: ~140 MB total (estimated)
Cost: $0.03-0.05 (estimated 1.5-2.5M tokens)

FAISS Indexes

Ready-to-use vector similarity search indexes:

L2 distance indexes: faiss_train_l2.index, faiss_valid_l2.index
Cosine similarity indexes: faiss_train_cosine.index, faiss_valid_cosine.index

Q&A Generation Strategies

Examples were generated using 5 complementary strategies:

Section-based Q&A: Questions from document sections
Control-based Q&A: NIST control requirements (3.1.1 format)
CMMC-specific Q&A: Level-focused questions (L1/L2/L3)
Domain-specific Q&A: Questions per CMMC domain
Semantic chunking: General content with context preservation

With weighted sampling: core documents (5x), balanced documents (3x), supplementary (2x).

Use Cases

The Comprehensive dataset is ideal for:

Enterprise-grade CMMC assistants: Maximum knowledge coverage
Research and development: Complete NIST CMMC corpus
Exhaustive RAG systems: Every relevant document included
Academic studies: Research-grade completeness
Specialized consulting: Coverage of niche/emerging topics
Long-term knowledge base: Future-proof comprehensive training

Dataset Statistics

Source Documents:         381
Total Examples:           11,279
Training Examples:        9,023 (80%)
Validation Examples:      2,256 (20%)
Avg Example Length:       ~234 tokens
Total Tokens Embedded:    2,639,168
Embedding Cost:           $0.05 USD
Domain Coverage:          Complete (all 17 domains)

Advantages Over Other Variants

vs. Core (14 docs, 1.2K examples):

27x more source documents
9x more training examples
Covers specialized/emerging topics not in core
Better for niche use cases and edge scenarios

vs. Balanced (71 docs, 2.8K examples):

5x more source documents
4x more training examples
Deeper coverage of each domain
Includes research reports and specialized guides
Better for exhaustive knowledge requirements

Trade-offs:

Longer training time (4x vs. Balanced)
Higher computational cost
May include redundant/overlapping content
Potential for overfitting on less-critical topics

When to Use Comprehensive

Choose Comprehensive if:

You need maximum coverage of CMMC topics
You're building an enterprise-grade knowledge system
You need coverage of specialized/emerging topics (IoT, cloud, supply chain)
Training time/cost is not a constraint
You want research-grade completeness
You're building a long-term knowledge base

Choose Balanced if:

You need good coverage but faster training
You want equal domain representation
You're resource-constrained
You need production-ready performance

Choose Core if:

You only need SP 800-171/172 fundamentals
You want fastest training possible
You're focused on core CMMC certification only

Example Topics Unique to Comprehensive

This variant includes specialized content not found in smaller datasets:

Cloud Security: NIST 800-series cloud guidance
IoT Security: Embedded systems and IoT frameworks
Supply Chain: Software supply chain security (SSDF, C-SCRM)
Privacy Engineering: NIST Privacy Framework integration
Quantum-Safe Crypto: Post-quantum cryptography guidance
Security Automation: SOAR and automation frameworks
Forensics: Digital forensics and incident investigation
Industrial Control Systems: ICS/SCADA security
Mobile Security: Mobile device management
Secure Development: SDLC and DevSecOps

Quick Start

Load JSONL Data

import json

# Load training data
with open('train.jsonl', 'r') as f:
    train_data = [json.loads(line) for line in f]

print(f"Total examples: {len(train_data)}")

# Example: Find all Level 3 examples
level3_examples = [
    ex for ex in train_data
    if ex.get('metadata', {}).get('cmmc_level') == '3'
]
print(f"Level 3 examples: {len(level3_examples)}")

Load Embeddings

import pandas as pd
import numpy as np

# Load embeddings
df = pd.read_parquet('embeddings_train.parquet')

# Access embeddings as numpy array
embeddings = np.vstack(df['embedding'].values)
texts = df['text'].tolist()

print(f"Embeddings shape: {embeddings.shape}")  # (9023, 1536)

Use FAISS Index for Semantic Search

import faiss
import numpy as np

# Load FAISS index
index = faiss.read_index('faiss_train_cosine.index')

# Search for similar content
query_embedding = ... # your query vector (1536-dim)
k = 10  # number of results
distances, indices = index.search(query_embedding.reshape(1, -1), k)

# Get similar texts with scores
for i, (idx, score) in enumerate(zip(indices[0], distances[0])):
    print(f"{i+1}. [Score: {score:.3f}] {texts[idx][:150]}...")

Related Datasets

This is part of a family of 3 CMMC datasets:

Core: 14 docs, 1.2K examples - Essential CMMC foundation
Balanced: 71 docs, 2.8K examples - Domain-balanced coverage
Comprehensive (this dataset): 381 docs, 11.3K examples - Complete NIST CMMC library

Citation

If you use this dataset, please cite:

@dataset{cmmc_comprehensive_2025,
  title={CMMC Training Dataset - Comprehensive Variant},
  author={Troy, Ethan Oliver},
  year={2025},
  publisher={HuggingFace},
  note={Derived from 381 NIST Special Publications (Public Domain)}
}

License

Public Domain - This dataset is derived from NIST Special Publications, which are works of the US Government and not subject to copyright protection in the United States.

Acknowledgments

This dataset is built from 381 publications by the National Institute of Standards and Technology (NIST), Computer Security Resource Center.

Special thanks to:

NIST CSRC for comprehensive cybersecurity documentation
The CMMC-AB for defining the certification framework
The open-source community for extraction and processing tools

Dataset Version

Version: 1.0
Created: 2025
Source: NIST CSRC Publications (381 documents)
Processing: Docling + custom CMMC-aware data preparation
Weighting: Core documents (5x), balanced (3x), supplementary (2x)

Performance Recommendations

For training with this large dataset:

Recommended batch size: 4-8 (depending on GPU memory)
Training iterations: 1000-2000 for good convergence
LoRA rank: 16-32 for capacity
Expected training time: 8-12 hours (7B model on M4 Max)
Memory required: 16GB+ for training, 64GB+ recommended

Contact

For questions or issues, please open an issue on the GitHub repository.