detecting-insider-threat-with-ueba

name	detecting-insider-threat-with-ueba
description	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. Use when implementing user and entity behavior analytics using elasticsearch/opensearch to build.
domain	cybersecurity
subdomain	threat-detection
tags	["ueba","insider-threat","anomaly-detection","elasticsearch","behavior-analytics","machine-learning","siem"]
version	1.0
author	oyi77
license	Apache-2.0
nist_csf	["DE.CM-01","DE.AE-02","DE.AE-06","ID.RA-05"]

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

Trigger phrases:

• "detecting insider threat with ueba"

• "Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to b"

• 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

# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}