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detecting-insider-threat-with-ueba

detecting-insider-threat-with-ueba

github.com/mukul975Security35.2k46 installsUpdated 8w ago
Repository

About detecting-insider-threat-with-ueba

A security skill for SOC analysts and threat hunters using UEBA to identify possible insider threats. It describes Elasticsearch-based behavioral baselines, anomaly scoring, and correlation of weak signals into alerts.

Key features

  1. 01Behavioral baselines from activity logs
  2. 02Per-user anomaly risk scoring
  3. 03Peer group comparisons
  4. 04Correlation of indicators into alerts
  5. 05JSON report with investigation actions

Use cases

  1. 01Investigating suspected insider activity
  2. 02Building UEBA threat detections
  3. 03Reviewing security monitoring coverage

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill detecting-insider-threat-with-ueba

After installing, type/detecting-insider-threat-with-uebain Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

LICENSE
references/
api-reference.md
scripts/
agent.py

Details

From the project README

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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