xpertsystems/cyb007-sample

CYB007 — Synthetic Insider Threat Dataset (Sample)

XpertSystems.ai Synthetic Data Platform · SKU: CYB007-SAMPLE · Version 1.0.0

This is a free preview of the full CYB007 — Synthetic Insider Threat Dataset product. It contains roughly ~10% of the full dataset at identical schema, actor-tier distribution, and statistical fingerprint, so you can evaluate fit before licensing the full product.

File	Rows (sample)	Rows (full)	Description
org_topology.csv	~240	~2,400	Department / org structure registry
incident_summary.csv	~500	~4,800	Per-incident aggregate outcomes
incident_events.csv	~38,687	~48,000	Discrete incident event log
insider_trajectories.csv	~32,500	~280,000	Per-timestep trajectory data (primary file)

Dataset Summary

CYB007 simulates end-to-end insider threat incident lifecycles as a 6-phase state machine across enterprise org topologies with calibrated UEBA defender modeling, covering:

• 4 actor threat-type tiers: negligent_user, malicious_employee, privileged_insider, compromised_account — with per-tier stealth weights, data access scopes, cover-tracks propensity, and collusion probabilities
• 8 UEBA defender statuses (graduated maturity ladder): no_ueba, dlp_only, siem_only, partial_coverage, pam_integrated, hr_integrated, full_coverage, zero_trust_enforced — each with distinct detection_strength and alert_suppression characteristics
• 6 lifecycle phases: reconnaissance, access_escalation, data_staging, exfiltration_attempt, cover_tracks, incident_resolution
• Exfiltration channels: email, USB, cloud upload, print, screen capture
• HR-trigger modeling — disgruntlement signals, performance reviews, resignation indicators, and behavioural anomalies that flag IR
• Collusion modeling — coordinated multi-actor incidents with weighted per-tier collusion probabilities
• Attribution risk scoring — recon intensity × stealth weight
• Sabotage outcomes — destructive insider actions distinct from exfiltration

Calibrated Benchmark Targets

The full product is calibrated to 12 benchmark validation tests drawn from authoritative insider threat research (CERT Insider Threat Center, Verizon DBIR, IBM Cost of Insider Threats, Ponemon Institute, MITRE ATT&CK, NIST SP 800-53 / SP 800-207, Securonix, Forrester UEBA, Gartner ZTNA, CrowdStrike, Mandiant M-Trends).

Sample benchmark results:

Test	Target	Observed	Verdict
exfiltration_success_rate_priv	0.1460	0.1524	✓ PASS
detection_rate_zero_trust	0.9100	0.9100	✓ PASS
alert_suppression_rate	0.0770	0.0798	✓ PASS
data_access_volume_mean_mb	220.0	151.5	~ MARGINAL
cover_tracks_rate	0.1800	0.1720	✓ PASS
dwell_time_ratio	0.2200	0.2212	✓ PASS
stealth_score_privileged	0.6800	0.7047	✓ PASS
hr_trigger_rate	0.1600	0.1220	✓ PASS
incident_success_rate	0.3400	0.3500	✓ PASS
lateral_access_rate	0.1100	0.1092	✓ PASS
collusion_rate	0.0850	0.0420	~ MARGINAL
attribution_risk_score	0.3100	0.2746	✓ PASS

Note: some benchmarks (e.g. privileged_insider exfil success rate, collusion rate, attribution risk) are conditional on smaller actor-tier subsets. The full product (4,800 incidents) demonstrates all 12 benchmarks at Grade A- or better with strong statistical power.

Schema Highlights

insider_trajectories.csv (primary file, per-timestep)

Column	Type	Description
incident_id	string	Unique incident identifier
actor_id	string	Insider actor ID
timestep	int	Step in 6-phase lifecycle (0–64)
phase	string	1 of 6 phases
data_access_volume_mb	float	Per-step data accessed
payload_entropy	float	Data payload entropy (0–8)
cover_actions_taken	int	Cover-tracks actions at this step
dlp_alerts_raised	int	DLP alerts triggered
detection_flag	int	Boolean — UEBA detection at this step
exfil_cumulative_mb	float	Cumulative exfiltrated data
blast_radius	float	Org-wide compromise score
sensitive_data_accessed	int	Boolean — sensitive data touched
threat_type_tier	string	negligent_user / malicious_employee / privileged_insider / compromised_account

incident_summary.csv (per-incident outcome)

Column	Type	Description
incident_id, actor_id	string	Identifiers
threat_type_tier	string	Tier classification target
ueba_status	string	Defender maturity tier
incident_success_flag	int	Boolean — incident succeeded
exfiltration_success_flag	int	Boolean — data exfiltrated
total_data_volume_mb	float	Total accessed in incident
exfiltrated_volume_mb	float	Total exfiltrated
cover_tracks_flag	int	Boolean — log tampering attempted
hr_trigger_flag	int	Boolean — HR-detected indicators
stealth_score	float	Overall stealth (0–1)
dwell_time_ratio	float	Fraction of timesteps in dwell
collusion_flag	int	Boolean — multi-actor coordination
attribution_risk_score	float	Likelihood of attribution (0–1)
lateral_access_flag	int	Boolean — out-of-scope dept access
sabotage_flag	int	Boolean — destructive action

See incident_events.csv and org_topology.csv for the discrete event log and department registry schemas respectively.

Suggested Use Cases

• Training insider threat classifier models (4-tier actor attribution)
• Data exfiltration detection modelling — DLP signal calibration
• UEBA effectiveness benchmarking — graduated 8-tier defender maturity
• HR-signal correlation — disgruntlement, resignation, performance triggers for early-warning systems
• Cover-tracks / log-tampering detection — stealth feature engineering
• Privileged access misuse detection (privileged_insider tier)
• Collusion detection — multi-actor coordinated incident patterns
• Attribution risk modelling — recon intensity × stealth
• Zero Trust posture validation — block rates by defender maturity tier
• Sabotage vs exfiltration discrimination

Loading the Data

import pandas as pd

trajectories = pd.read_csv("insider_trajectories.csv")
incidents    = pd.read_csv("incident_summary.csv")
events       = pd.read_csv("incident_events.csv")
topology     = pd.read_csv("org_topology.csv")

# Join trajectory data with incident-level labels
enriched = trajectories.merge(incidents, on=["incident_id", "actor_id"],
                              how="left", suffixes=("", "_summary"))

# 4-class threat-type classification target
y_tier = incidents["threat_type_tier"]

# Binary exfiltration-success target
y_exfil = incidents["exfiltration_success_flag"]

# Binary collusion target
y_collusion = incidents["collusion_flag"]

License

This sample is released under CC-BY-NC-4.0 (free for non-commercial research and evaluation). The full production dataset is licensed commercially — contact XpertSystems.ai for licensing terms.

Full Product

The full CYB007 dataset includes ~335,000 rows across all four files, with calibrated benchmark validation against 12 metrics drawn from authoritative insider threat research sources.

📧 pradeep@xpertsystems.ai 🌐 https://xpertsystems.ai

Citation

@dataset{xpertsystems_cyb007_sample_2026,
  title  = {CYB007: Synthetic Insider Threat Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/cyb007-sample}
}

Generation Details

• Generator version : 1.0.0
• Random seed : 42
• Generated : 2026-05-16 14:17:56 UTC
• Lifecycle model : 6-phase insider threat state machine
• Overall benchmark : 95.3 / 100 (grade A)