detecting-insider-threat-with-ueba

Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access patterns.

4,032 stars

Best use case

detecting-insider-threat-with-ueba is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access patterns.

Teams using detecting-insider-threat-with-ueba should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/detecting-insider-threat-with-ueba/SKILL.md --create-dirs "https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/detecting-insider-threat-with-ueba/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/detecting-insider-threat-with-ueba/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How detecting-insider-threat-with-ueba Compares

Feature / Agentdetecting-insider-threat-with-uebaStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access patterns.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# 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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