Detecting Insider Data Exfiltration
FreeIdentify insider threats through data loss prevention analytics.
Free · Opens the source repo
What Detecting Insider Data Exfiltration does
Detecting Insider Data Exfiltration via DLP is a Python-based skill designed for security operations professionals focused on identifying and mitigating insider threats. This skill leverages behavioral analytics and statistical anomaly detection to analyze data loss prevention (DLP) policy violations, file access patterns, upload volume anomalies, and off-hours activity. By utilizing the pandas library, it provides a structured approach to understanding user behavior in relation to data exfiltration risks.
The skill is particularly useful for security analysts who need to investigate incidents involving potential insider threats. It helps in building detection rules or threat-hunting queries, providing a clear methodology for analyzing endpoint activity logs, cloud storage access, and email DLP events. By establishing behavioral baselines, users can detect abnormal patterns that may indicate malicious intent, such as excessive data uploads or unusual access times.
To effectively use this skill, users should have a foundational understanding of security operations concepts, access to a test environment, and the necessary Python setup. The skill includes examples of how to implement detection algorithms, making it easier for analysts to apply the techniques in real-world scenarios. Key indicators for detection include upload volumes exceeding established baselines, off-hours file access, and unusual access patterns, all of which are critical for maintaining data security in an organization.
Overall, this skill is a valuable tool for security operations centers (SOCs) and analysts tasked with safeguarding sensitive information against insider threats, enabling them to validate security monitoring coverage and respond effectively to potential data breaches.
When to use it
Use this skill when investigating potential insider threats or when developing user behavior analytics for data loss prevention.
When not to use it
This skill may not be suitable for environments lacking proper logging or where data access patterns are not well-defined.
What you can build with it
Investigating a Security Incident
Use this skill to analyze logs when a potential insider threat is reported, helping to identify unusual data access patterns.
Building Detection Rules
Employ the skill to create robust detection rules for monitoring user behavior and preventing data loss.
Validating Security Coverage
Utilize the skill to assess existing security measures and ensure they cover potential insider threat scenarios.
How to install Detecting Insider Data Exfiltration
View source1. Install with the skills CLI
npx skills add mukul975/anthropic-cybersecurity-skills/detecting-insider-data-exfiltration-via-dlp --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 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.
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:
- Upload volume exceeding 3x daily baseline
- Access to files outside normal scope
- Bulk downloads before resignation
- Off-hours file access patterns
- USB/external device usage spikes
Examples
# 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)
Frequently asked questions about Detecting Insider Data Exfiltration
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