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mukul975
efca3ec611
Mapped every skill to NIST CSF 2.0 subcategory IDs (GV/ID/PR/DE/RS/RC functions) based on subdomain and content analysis. Restores 11 skills corrupted during prior rebase, re-enriching with ATLAS, D3FEND, NIST AI RMF, and CSF 2.0 fields. All 754 skills now carry structured mappings for all 5 security frameworks: - MITRE ATT&CK (in tags) - MITRE ATLAS v5.5 (atlas_techniques) - MITRE D3FEND v1.3 (d3fend_techniques) - NIST AI RMF 1.0 (nist_ai_rmf) - NIST CSF 2.0 (nist_csf)
76 lines
2.3 KiB
Markdown
76 lines
2.3 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, file access patterns, upload volume anomalies,
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and off-hours activity in endpoint and cloud logs. Uses pandas for behavioral analytics and statistical baselines. Use when
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investigating insider threats or building user behavior analytics for data 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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- detecting
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- insider
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- data
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- exfiltration
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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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---
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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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