Detecting Insider Threat with UEBA
FreeLeverage UEBA for effective insider threat detection.
Free · Opens the source repo
What Detecting Insider Threat with UEBA does
Detecting insider threats is a critical aspect of modern cybersecurity, and User and Entity Behavior Analytics (UEBA) provides a robust framework for identifying unusual patterns of activity that may indicate malicious behavior. This skill utilizes Elasticsearch or OpenSearch as the backend for analyzing user and entity behaviors, allowing security teams to establish behavioral baselines from various log sources such as authentication logs, file access events, and network activity. By modeling normal behavior, the skill can flag statistically significant deviations that warrant further investigation.
The process begins with ingesting and normalizing activity logs from diverse sources like Active Directory, VPNs, and file servers. Once the logs are ingested, the skill calculates per-user behavioral baselines over a rolling 30-day window, which includes metrics like login times and data access patterns. These baselines are then used to compute anomaly scores through z-score deviations and peer group comparisons, enabling the identification of potential insider threats.
Alerts are generated when multiple anomalous indicators are detected, such as unusual access times combined with large data downloads. The output is a JSON report detailing per-user risk scores, anomalous activities, and recommended actions for Security Operations Center (SOC) analysts. This structured approach not only enhances the detection capabilities of security teams but also streamlines the investigation process, making it an essential tool for organizations aiming to bolster their insider threat detection strategies.
This skill is particularly useful for security professionals looking to implement or refine a UEBA pipeline, providing a systematic method to analyze user behavior and respond to potential threats effectively. By focusing on behavioral analytics, it moves beyond traditional static rule-based systems, offering a more dynamic and accurate method for identifying insider threats.
When to use it
Use this skill when you need to investigate insider threats or build detection rules based on user behavior analytics.
When not to use it
This skill is not suitable for environments without sufficient historical user activity data or where Elasticsearch/OpenSearch is not deployed.
What you can build with it
Investigating a Security Incident
Use this skill to analyze user behavior during a suspected insider threat incident, providing insights into abnormal activities.
Building Detection Rules
Leverage the skill to create tailored detection rules based on historical user behavior and peer group analysis.
Validating Security Monitoring Coverage
Employ this skill to assess and improve your organization's security monitoring capabilities against insider threats.
How to install Detecting Insider Threat with UEBA
View source1. Install with the skills CLI
npx skills add mukul975/anthropic-cybersecurity-skills/detecting-insider-threat-with-ueba --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by mukul975Detecting 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.
Frequently asked questions about Detecting Insider Threat with UEBA
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