detecting-insider-threat-behaviors

name	detecting-insider-threat-behaviors
description	Detect insider threat behavioral indicators including unusual data access, off-hours activity, mass file downloads, privilege abuse, and resignation-correlated data theft. Use when detecting insider threat behavioral indicators including unusual data access, off-hours.
domain	cybersecurity
tags	["threat-hunting","mitre-attack","insider-threat","data-theft","ueba","proactive-detection"]
subdomain	threat-hunting
version	1.0
author	oyi77
license	Apache-2.0
d3fend_techniques	["Restore Access","Password Authentication","Biometric Authentication","Strong Password Policy","Restore User Account Access"]
nist_csf	["DE.CM-01","DE.AE-02","DE.AE-07","ID.RA-05"]

Detecting Insider Threat Behaviors

Overview

Cybersecurity skill for detecting insider threat behaviors. Follows industry best practices and security standards.

When to Use

Trigger phrases:

• "detecting insider threat behaviors"

• "When proactively hunting for indicators of detecting insider threat behaviors in"

• "After threat intelligence indicates active campaigns using these techniques"

• "During incident response to scope compromise related to these techniques"

• When proactively hunting for indicators of detecting insider threat behaviors in the environment

• After threat intelligence indicates active campaigns using these techniques

• During incident response to scope compromise related to these techniques

• When EDR or SIEM alerts trigger on related indicators

• During periodic security assessments and purple team exercises

When NOT to Use

• When you lack proper authorization for testing
• For production systems without change management
• When the task requires legal or compliance expertise beyond technical scope

Prerequisites

• EDR platform with process and network telemetry (CrowdStrike, MDE, SentinelOne)
• SIEM with relevant log data ingested (Splunk, Elastic, Sentinel)
• Sysmon deployed with comprehensive configuration
• Windows Security Event Log forwarding enabled
• Threat intelligence feeds for IOC correlation

Workflow

# 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)  k, v  IOC_PATTERNS.items()}