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detecting-insider-threat-with-ueba

Build or tune a monitoring pipeline that learns normal user behavior from activity logs. Use Elasticsearch or OpenSearch to score unusual activity, compare colleagues in similar roles, and combine warning signs into insider threat alerts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

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.

실행 점수 미검증

설치 방법 보기

이럴 때 유용합니다

  • Building continuous monitoring for insider threats
  • Scoring unusual logins and large downloads
  • Comparing behavior across similar job roles
  • Reviewing user risk scores and investigation priorities

포함된 파일(4)

  • SKILL.md2.9 KB
  • LICENSE11.0 KB
  • references/api-reference.md2.4 KB
  • scripts/agent.py8.2 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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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