seikaikyo/detecting-insider-threat-with-ueba
external/anthropic-cybersecurity-skills/skills/detecting-insider-threat-with-ueba/SKILL.md
Implement User and Entity Behavior Analytics (UEBA) using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and alert on insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access. Use when building or tuning a UEBA pipeline rather than a one-off manual hunt.
$ install --global
skillsauthnpx skillsauth add seikaikyo/dash-skills detecting-insider-threat-with-uebaInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Aug 6, 2026, 3:08 AM313.9s3 files scanned
SKILL.md
- name:
- detecting-insider-threat-with-ueba
- description:
- Implement User and Entity Behavior Analytics (UEBA) using Elasticsearch/OpenSearch
- domain:
- cybersecurity
- subdomain:
- threat-detection
- version:
- 1.0
- author:
- mahipal
- license:
- Apache-2.0
Detecting Insider Threat with UEBA
Overview
User and Entity Behavior Analytics (UEBA) moves beyond static rule-based detection to model normal behavior for users, hosts, and applications, then flag statistically significant deviations that may indicate insider threats. Using Elasticsearch as the analytics backend, this skill covers building behavioral baselines from authentication logs, file access events, and network activity, computing risk scores using statistical deviation and peer group comparison, and correlating multiple low-confidence indicators into high-confidence insider threat alerts.
When to Use
- When investigating security incidents that require detecting insider threat with ueba
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Elasticsearch 8.x or OpenSearch 2.x cluster with security audit data
- Log sources: Active Directory authentication, VPN, DLP, file server access, email
- Python 3.9+ with elasticsearch client library
- Baseline period of 30+ days of normal user activity data
- Defined peer groups based on department, role, or job function
Steps
Step 1: Ingest and Normalize Activity Logs
Configure log pipelines to ingest authentication, file access, email, and network logs into Elasticsearch with a unified user identity field.
Step 2: Build Behavioral Baselines
Calculate per-user baselines for login times, data volume, application usage, and access patterns over a rolling 30-day window using Elasticsearch aggregations.
Step 3: Calculate Anomaly Scores
Compare current activity against baselines using z-score deviation and peer group comparison to generate per-user risk scores.
Step 4: Correlate and Alert
Combine multiple anomalous indicators (unusual hours + large downloads + new system access) into composite risk scores that trigger SOC investigation workflows.
Expected Output
JSON report containing per-user risk scores, anomalous activity details, peer group deviations, and recommended investigation actions.
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