Datasets:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
niche_id: string
niche_name: string
category: string
record_count: int64
expected_fields: list<item: string>
child 0, item: string
descriptive_fields: list<item: string>
child 0, item: string
quality_score: int64
passed: bool
schema: struct<valid_records: int64, conformance_pct: double, missing_fields: struct<>, empty_fields: struct (... 27 chars omitted)
child 0, valid_records: int64
child 1, conformance_pct: double
child 2, missing_fields: struct<>
child 3, empty_fields: struct<>
child 4, extra_fields: struct<>
uniqueness: struct<duplicate_rows: int64, duplicate_identity_values: int64, duplicate_fields: struct<>>
child 0, duplicate_rows: int64
child 1, duplicate_identity_values: int64
child 2, duplicate_fields: struct<>
text_depth: struct<field_count: int64, avg_chars: double, min_chars: int64, avg_words: double, min_words: int64, (... 73 chars omitted)
child 0, field_count: int64
child 1, avg_chars: double
child 2, min_chars: int64
child 3, avg_words: double
child 4, min_words: int64
child 5, vocabulary_diversity: double
child 6, total_tokens: int64
child 7, unique_tokens: int64
similarity: struct<max_similarity: double, avg_similarity: double, near_duplicate_pairs: int64>
child 0, max_similarity: double
child 1, avg_similarity: double
child 2, near_duplicate_pairs: int64
repetition: struct<repeated_ngram_count: int64, repeated_ngram_rate: double, top_repeated_phrases: list<item: st (... 100 chars omitted)
child 0, repeated_ngram
...
ted)
child 0, fields: struct<threat_category: struct<unique_values: int64, top_share: double, values: struct<Malware infec (... 94 chars omitted)
child 0, threat_category: struct<unique_values: int64, top_share: double, values: struct<Malware infection: int64, Unauthorize (... 69 chars omitted)
child 0, unique_values: int64
child 1, top_share: double
child 2, values: struct<Malware infection: int64, Unauthorized access: int64, Brute-force attack: int64, Phishing att (... 12 chars omitted)
child 0, Malware infection: int64
child 1, Unauthorized access: int64
child 2, Brute-force attack: int64
child 3, Phishing attempt: int64
findings: list<item: struct<severity: string, message: string>>
child 0, item: struct<severity: string, message: string>
child 0, severity: string
child 1, message: string
content: struct<rejected_record_count: int64, rejections: list<item: null>>
child 0, rejected_record_count: int64
child 1, rejections: list<item: null>
child 0, item: null
verification: struct<all_rows_checked: int64, similarity_is_bounded: bool, max_similarity_pairs: int64>
child 0, all_rows_checked: int64
child 1, similarity_is_bounded: bool
child 2, max_similarity_pairs: int64
data_sha256: string
repair_source_revision: string
alert_id: string
analyst_assessment: string
analyst_name: string
system_impacted: string
containment_steps: string
threat_category: string
to
{'alert_id': Value('string'), 'analyst_name': Value('string'), 'threat_category': Value('string'), 'system_impacted': Value('string'), 'analyst_assessment': Value('string'), 'containment_steps': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
niche_id: string
niche_name: string
category: string
record_count: int64
expected_fields: list<item: string>
child 0, item: string
descriptive_fields: list<item: string>
child 0, item: string
quality_score: int64
passed: bool
schema: struct<valid_records: int64, conformance_pct: double, missing_fields: struct<>, empty_fields: struct (... 27 chars omitted)
child 0, valid_records: int64
child 1, conformance_pct: double
child 2, missing_fields: struct<>
child 3, empty_fields: struct<>
child 4, extra_fields: struct<>
uniqueness: struct<duplicate_rows: int64, duplicate_identity_values: int64, duplicate_fields: struct<>>
child 0, duplicate_rows: int64
child 1, duplicate_identity_values: int64
child 2, duplicate_fields: struct<>
text_depth: struct<field_count: int64, avg_chars: double, min_chars: int64, avg_words: double, min_words: int64, (... 73 chars omitted)
child 0, field_count: int64
child 1, avg_chars: double
child 2, min_chars: int64
child 3, avg_words: double
child 4, min_words: int64
child 5, vocabulary_diversity: double
child 6, total_tokens: int64
child 7, unique_tokens: int64
similarity: struct<max_similarity: double, avg_similarity: double, near_duplicate_pairs: int64>
child 0, max_similarity: double
child 1, avg_similarity: double
child 2, near_duplicate_pairs: int64
repetition: struct<repeated_ngram_count: int64, repeated_ngram_rate: double, top_repeated_phrases: list<item: st (... 100 chars omitted)
child 0, repeated_ngram
...
ted)
child 0, fields: struct<threat_category: struct<unique_values: int64, top_share: double, values: struct<Malware infec (... 94 chars omitted)
child 0, threat_category: struct<unique_values: int64, top_share: double, values: struct<Malware infection: int64, Unauthorize (... 69 chars omitted)
child 0, unique_values: int64
child 1, top_share: double
child 2, values: struct<Malware infection: int64, Unauthorized access: int64, Brute-force attack: int64, Phishing att (... 12 chars omitted)
child 0, Malware infection: int64
child 1, Unauthorized access: int64
child 2, Brute-force attack: int64
child 3, Phishing attempt: int64
findings: list<item: struct<severity: string, message: string>>
child 0, item: struct<severity: string, message: string>
child 0, severity: string
child 1, message: string
content: struct<rejected_record_count: int64, rejections: list<item: null>>
child 0, rejected_record_count: int64
child 1, rejections: list<item: null>
child 0, item: null
verification: struct<all_rows_checked: int64, similarity_is_bounded: bool, max_similarity_pairs: int64>
child 0, all_rows_checked: int64
child 1, similarity_is_bounded: bool
child 2, max_similarity_pairs: int64
data_sha256: string
repair_source_revision: string
alert_id: string
analyst_assessment: string
analyst_name: string
system_impacted: string
containment_steps: string
threat_category: string
to
{'alert_id': Value('string'), 'analyst_name': Value('string'), 'threat_category': Value('string'), 'system_impacted': Value('string'), 'analyst_assessment': Value('string'), 'containment_steps': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Soc_Alert_Incident_Tickets1 (Synthetic B2B Dataset Preview)
Add me on Discord: xomohappy for access support, delivery questions, or product questions about this premade commercial dataset.
This is a premium, privacy-compliant, industry-safe synthetic dataset simulating Security Operations Center (SOC) Alert & Incident logs for B2B applications.
About this Dataset
This dataset is generated programmatically using large language models combined with a strict data curation and validation layer.
- Privacy-safe: Contains synthetic names, phone numbers, and company identifiers.
- Structured and verified: Schema-validated, formatted, and deduplicated to reduce model overfitting risk.
- Target AI use case: Cybersecurity firms training automated incident response and threat triagers.
- Category: Security
Schema Definition
Each record contains the following fields:
alert_id(str): Pre-assigned Alert ID (e.g. SOC-INC-XXXX)analyst_name(str): Pre-assigned Security Analyst Namethreat_category(str): Malware infection, brute-force attack, phishing attempt, or unauthorized accesssystem_impacted(str): Name of server, cloud database, or workstationanalyst_assessment(str): Security analyst's assessment of threat level and compromise evidencecontainment_steps(str): Technical steps taken to isolate host, block IP, and remediate threat
Get the Commercial Version
Need a larger dataset for production fine-tuning? The matching private repo is HaseebDev/soc_alert_incident_tickets1-commercial and is available under a commercial license.
- Payment: Lemon Squeezy hosted checkout.
- Delivery: Lemon Squeezy checkout with secure commercial delivery after purchase
- Typical commercial package: 10,000+ records, schema documentation, and quality audit report.
Open the commercial access page
Quality Audit
Dataset Quality Report
Executive Summary
| Metric | Value |
|---|---|
| Status | PASS |
| Quality Score | 94/100 |
| Records Audited | 100 |
| Niche | Security Operations Center (SOC) Alert & Incident logs |
| Category | Security |
Quality Gates
| Dimension | Result |
|---|---|
| Schema Conformance | 100.0% (100/100) |
| Duplicate Rows | 0 |
| Duplicate Identity Values | 0 |
| Non-Synthetic Phone Risk | 0 |
| Email Address Count | 0 |
| Max Fuzzy Similarity | 0.558 |
| Average Words Per Descriptive Field | 72.81 |
| Vocabulary Diversity | 0.84 |
| Repeated Phrase Count | 11 |
| Domain Keyword Coverage | 0.667 |
Buyer Assurance
This dataset was checked with deterministic local tooling before publication. The audit verifies schema consistency, uniqueness, text depth, duplicate risk, repeated wording, domain terminology coverage, and obvious PII leakage patterns.
All rows receive schema and content checks. Cross-record similarity uses a bounded sample of pairs to keep generation fast; it does not establish that every possible pair is dissimilar. The score measures these automated checks, not downstream model performance or independent medical, legal, or regulatory validation.
Findings
- MINOR: 11 high-frequency non-domain four-word phrase pattern(s) detected.
Data SHA-256: 26bcb7214935808b689e3656c3c1eb3c80b01e4c9fa7691ca2ab684ddeaf7168. Re-audited on 2026-10-08.
Repair: defective fictional narratives were corrected; record IDs and synthetic profile identities were retained. Legacy previews now cover all six scenario classes.
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