layers/cloud_security/DATASET_CARD.md

HolyCyber — Layer: cloud_security

A focused dataset for the cloud_security domain of the HolyCyber corpus, built on its own from that domain's authoritative sources only.

Overview

• Rows: 2,254
• Groups (leakage units): 590
• Entities in the domain: 590
• Splits: test=218, train=1,804, val=232
• Task types: 7 | Subdomains: 5
• Row size (tiktoken cl100k, user+assistant): min 2,076 / median 2,313 / mean 2,356 / p90 2,521 / max 5,288 tokens
• Entity's own material: median 18% of an answer's tokens; 1,548 rows are below 25% (the balance is the labelled reference appendix)
• Total tokens: 5,310,326 | Rows below the 2,000-token floor: 0
• Canary: every row carries the decontamination canary

Record format

One JSON object per line; messages is a three-turn chat (system / user / assistant).

{"id": "6d408e38f3cb8dff", "row_key": "<domain>:<group_key>:<task_type>:<subdomain>:<part>",
 "domain": "cloud_security", "subdomain": "...",
 "task_type": "...", "difficulty": "intermediate", "group_key": "...",
 "source": "...", "license": "...", "split": "train", "canary": "...",
 "tokens": {"user": 180, "assistant": 2350, "total": 2530},
 "core_share": 0.41, "appendix": true,
 "references": [{"title": "...", "url": "..."}],
 "messages": [{"role": "system", "content": "..."},
              {"role": "user", "content": "..."},
              {"role": "assistant", "content": "## ...\n..."}]}

Field	Meaning
id	content hash of user+assistant, stable across rebuilds of the same row
row_key	stable identity across rebuilds (domain:group:task:subdomain:part)
domain / subdomain	this layer, and the slice of it the row covers
task_type	the question shape (detection, mitigation, mapping, ...)
difficulty	derived from the task shape, not guessed
group_key	the source entity; the unit of the train/val/test split
source / license	the authoritative source, and its licence
tokens	measured cl100k_base counts for user, assistant and total
core_share	share of the answer that is the entity's own material (1 - appendix)
appendix	whether the row carries a ## Reference: appendix
references	resolvable links for the entities cited in the answer
canary	decontamination marker

What a row contains

Topic sections used in this layer's answers (structural labels such as Answer or Context and the appendix are excluded):

Section	Rows
Summary	2,106
Detection strategies and analytics	945
Tracked users (groups, malware, tools)	818
Mitigations	720
Background	557
Classification	543
Scope note	543
Relevant mitigations	458
Sub-techniques	85
Introduction	26
Abstract	15
Conclusion	11

How this layer is built

1. Base cells — the rows the source parsers emit for this domain (one per entity x task type x subdomain).
2. Task shapes — the same entity asked a different defensive question (detection, mitigation, framework mapping, response), built only from other sections of that entity's own record. No new facts are introduced.
3. Reference pack — a row still under the token floor is padded with compact descriptions of other entities of this domain, taken from this domain's own source text; the window rotates per row.

Every row therefore comes from this domain's sources only, clears the 2,000-token floor, and no two rows are the same.

Task types

Task type	Rows
knowledge_explanation	693
control_mapping	543
detection_engineering	405
threat_attribution	346
mitigation_remediation	261
definition	5
ai_security_analysis	1

Subdomains

Subdomain	Rows
audit_logging	751
compute_abuse	696
container_security	544
identity_posture	262
saas_abuse	1

Sources

Source	Rows
mitre-attack	2,103
arxiv-corpus	118
ietf-rfc	14
nist-publication	11
owasp-cheatsheet	5
research-corpus	3

Quality controls

• Exact duplicates removed: 0
• Near-duplicates removed: 1 (MinHash-LSH, Jaccard >= 0.90, same task type only)
• Quality gate: 2 rejected ({'unresolved_markdown_link': 2})
• Contaminated rows removed: 0 ({})
• Split integrity: group-level by group_key; no group crosses a split

Intended use

Supervised fine-tuning / evaluation of a defensive cybersecurity assistant specialised in this domain. All rows are defensive, detection or analysis oriented; none contains functional exploit code or weaponised prompts.

from datasets import load_dataset
ds = load_dataset("json", data_files=dict(train="cloud_security/train.jsonl", validation="cloud_security/val.jsonl", test="cloud_security/test.jsonl"))
# rows whose answer is mostly the entity itself, not the appendix
core = ds["train"].filter(lambda r: r["core_share"] >= 0.5)

Limitations

• 1,548 of 2,254 rows are more than 75% labelled reference appendix: this domain's own records are shorter than the 2,000-token floor, so the row is completed with other entities' records. Mask the appendix when fine-tuning if you only want the core answer.

• Paragraph-level reuse is measured in research/AUDIT_REPORT.md; rows are unique but their appendix material is shared.