Individual skill
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. Uses pandas for behavioral analytics and statistical baselines. Use when investigating insider threats or building user behavior analytics for data loss prevention.
internal/tools/skills/data/security-operations/detecting-insider-data-exfiltration-via-dlp/SKILL.md
Source commit d42f381ad34843e71a42658e5c57290c713e85af
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- Last checked
- Nov 7, 2026
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- 1,313
- Repository last updated
- Nov 7, 2026
- License
- Apache-2.0
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- cybersecurity Repository topic
- security Repository topic
- bug-bounty Repository topic
- automation Repository topic
- autonomous-pentesting Repository topic
- ai-agent Repository topic
- ai-security Repository topic
- ethical-hacking Repository topic
- golang Repository topic
- penetration-testing Repository topic
- detecting Declared skill tag
- insider Declared skill tag
- data Declared skill tag
- exfiltration Declared skill tag
- data Derived category
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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
## Detection Gaps & Validation
- **Channel coverage gaps are the main false negative:** volume baselines over endpoint/cloud/email logs miss exfil over DNS tunneling, DoH/DoT (encrypted DNS bypasses URL filtering), personal webmail/cloud (Gmail, personal OneDrive/Dropbox), Slack/Discord uploads, and pastebin/git pushes. If the DLP has no sensor on that channel, the 3x-baseline rule never sees the bytes. Enumerate covered channels explicitly and treat uncovered ones as blind spots.
- **Low-and-slow defeats the 3x threshold:** an insider drip-feeding a few MB/day stays under `user_avg * 3` forever. Add cumulative-over-time (e.g., 30-day rolling sum vs. role peers) and rare-destination detection, not just same-day spikes.