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mukul975
cb8d79e068
- Add validated mitre_attack frontmatter to all 754 skills (286 distinct techniques), verified against MITRE ATT&CK v19.1 via the official mitreattack-python library: 0 revoked, deprecated, or invalid IDs - Curate precise per-skill technique IDs for forensics, malware-analysis, threat-intel, and red-team skills (e.g. DCSync -> T1003.006, Kerberoasting -> T1558.003, Pass-the-Ticket -> T1550.003) - Reconcile v19.1 tactic restructuring: Defense Evasion split into Stealth (TA0005) and Defense Impairment (TA0112); revoked T1562.* family and T1070.001/.002 remapped to active equivalents (T1685.*) - Normalize word-split tags across 35 skills (remove filename-derived stopword tags, add semantic cybersecurity tags) - Add api-reference.md for 3 skills that were missing it - Update README ATT&CK section with accurate v19.1 tactic distribution
86 lines
2.4 KiB
Markdown
86 lines
2.4 KiB
Markdown
---
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name: detecting-insider-data-exfiltration-via-dlp
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description: 'Detects insider data exfiltration by analyzing DLP policy violations,
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file access patterns, upload volume anomalies, and off-hours activity in endpoint
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and cloud logs. Uses pandas for behavioral analytics and statistical baselines.
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Use when investigating insider threats or building user behavior analytics for data
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loss prevention.
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'
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domain: cybersecurity
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subdomain: security-operations
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tags:
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- insider-threat
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- data-loss-prevention
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- dlp
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- exfiltration-detection
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- ueba
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- security-operations
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version: '1.0'
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author: mahipal
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license: Apache-2.0
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nist_csf:
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- DE.CM-01
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- RS.MA-01
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- GV.OV-01
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- DE.AE-02
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mitre_attack:
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- T1078
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- T1190
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- T1059
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- T1048
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- T1041
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---
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# Detecting Insider Data Exfiltration via DLP
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## When to Use
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- When investigating security incidents that require detecting insider data exfiltration via dlp
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- When building detection rules or threat hunting queries for this domain
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- When SOC analysts need structured procedures for this analysis type
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- When validating security monitoring coverage for related attack techniques
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## Prerequisites
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- Familiarity with security operations concepts and tools
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- Access to a test or lab environment for safe execution
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- Python 3.8+ with required dependencies installed
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- Appropriate authorization for any testing activities
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## Instructions
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Analyze endpoint activity logs, cloud storage access, and email DLP events to detect
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data exfiltration patterns using behavioral baselines and statistical anomaly detection.
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```python
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import pandas as pd
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df = pd.read_csv("file_activity.csv", parse_dates=["timestamp"])
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# Baseline: average daily upload volume per user
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baseline = df.groupby(["user", df["timestamp"].dt.date])["bytes_transferred"].sum()
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user_avg = baseline.groupby("user").mean()
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# Alert on users exceeding 3x their baseline
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today = df[df["timestamp"].dt.date == pd.Timestamp.today().date()]
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today_totals = today.groupby("user")["bytes_transferred"].sum()
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anomalies = today_totals[today_totals > user_avg * 3]
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```
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Key indicators:
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1. Upload volume exceeding 3x daily baseline
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2. Access to files outside normal scope
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3. Bulk downloads before resignation
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4. Off-hours file access patterns
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5. USB/external device usage spikes
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## Examples
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```python
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# Detect off-hours activity
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df["hour"] = df["timestamp"].dt.hour
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off_hours = df[(df["hour"] < 6) | (df["hour"] > 22)]
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suspicious = off_hours.groupby("user").size().sort_values(ascending=False)
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```
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