# detecting-insider-data-exfiltration-via-dlp

> Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs using pandas for behavioral analytics and statistical baselines.

- Skill: `mukul975/detecting-insider-data-exfiltration-via-dlp` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds add mukul975/detecting-insider-data-exfiltration-via-dlp`
- Raw SKILL.md: https://api.skillmd.com/api/skills/mukul975/detecting-insider-data-exfiltration-via-dlp/raw
- Safety review: CAUTION (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Security, Data & Analytics, DevOps & Infra, Data Analysis, Incident Response
- Tags: Anomaly Detection, Behavioral Analytics, Cloud Logs, Data Exfiltration, Dlp, Endpoint Logs, Insider Threat, Pandas
- License: Apache-2.0
- Author: mukul975 (https://skillmd.com/u/mukul975)
- Updated: 2026-07-06
- Page: https://skillmd.com/skills/mukul975/detecting-insider-data-exfiltration-via-dlp

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# Detecting Insider Data Exfiltration via DLP


## When to Use

- 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

## 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

## Instructions

Analyze endpoint activity logs, cloud storage access, and email DLP events to detect
data exfiltration patterns using behavioral baselines and statistical anomaly detection.

```python
import pandas as pd

df = pd.read_csv("file_activity.csv", parse_dates=["timestamp"])
# Baseline: average daily upload volume per user
baseline = df.groupby(["user", df["timestamp"].dt.date])["bytes_transferred"].sum()
user_avg = baseline.groupby("user").mean()

# Alert on users exceeding 3x their baseline
today = df[df["timestamp"].dt.date == pd.Timestamp.today().date()]
today_totals = today.groupby("user")["bytes_transferred"].sum()
anomalies = today_totals[today_totals > user_avg * 3]
```

Key indicators:
1. Upload volume exceeding 3x daily baseline
2. Access to files outside normal scope
3. Bulk downloads before resignation
4. Off-hours file access patterns
5. USB/external device usage spikes

## Examples

```python
# Detect off-hours activity
df["hour"] = df["timestamp"].dt.hour
off_hours = df[(df["hour"] < 6) | (df["hour"] > 22)]
suspicious = off_hours.groupby("user").size().sort_values(ascending=False)
```