mukul975/detecting-insider-data-exfiltration-via-dlp
skills/detecting-insider-data-exfiltration-via-dlp/SKILL.md
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.
$ install --global
skillsauthnpx skillsauth add mukul975/anthropic-cybersecurity-skills detecting-insider-data-exfiltration-via-dlpInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
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3
Scanners Passed
9
Scanners in report
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TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
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OWASP Dep-Check
50%
Last scanned: Jun 6, 2026, 5:15 AM147.7s3 files scanned
SKILL.md
- name:
- detecting-insider-data-exfiltration-via-dlp
- description:
- Detects insider data exfiltration by analyzing DLP policy violations,
- domain:
- cybersecurity
- subdomain:
- security-operations
- version:
- 1.0
- author:
- mahipal
- license:
- Apache-2.0
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.
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)
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