detecting-insider-data-exfiltration-via-dlp

name	detecting-insider-data-exfiltration-via-dlp
description	Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs. Uses pandas for behavioral analytics and statistical baselines. Use when investigating insider threats or building user behavior analytics for data loss prevention.
domain	cybersecurity
tags	["detecting","insider","data","exfiltration"]
subdomain	security-operations
version	1.0
author	oyi77
license	Apache-2.0
nist_csf	["DE.CM-01","RS.MA-01","GV.OV-01","DE.AE-02"]

Detecting Insider Data Exfiltration Via Dlp

Overview

Cybersecurity skill for detecting insider data exfiltration via dlp. Follows industry best practices and security standards.

When to Use

Trigger phrases:

• "detecting insider data exfiltration via dlp"

• "When investigating security incidents that require detecting insider data exfilt"

• "When building detection rules or threat hunting queries for this domain"

• "When SOC analysts need structured procedures for this analysis type"

• When investigating security incidents that require detecting insider data exfiltration via dlp

• 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

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

• Familiarity with security operations concepts and tools
• Access to a test or lab environment for safe execution
• Python 3.8+ with required dependencies installed
• Appropriate authorization for any testing activities

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) for k, v in IOC_PATTERNS.items()}