본문으로 건너뛰기
cccskills
무료GitHub에 공개

detecting-insider-data-exfiltration-via-dlp

Analyze endpoint, cloud storage, and email logs for signs of insider data theft. Use pandas to compare upload volumes with normal user behavior and examine data loss prevention alerts, unusual file access, and activity outside working hours.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

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.

실행 점수 미검증

설치 방법 보기

이럴 때 유용합니다

  • Investigating suspected insider data theft
  • Finding users with unusually large uploads
  • Reviewing file access outside working hours
  • Developing detection rules for data loss prevention

포함된 파일(4)

  • SKILL.md2.4 KB
  • LICENSE11.0 KB
  • references/api-reference.md1.7 KB
  • scripts/agent.py7.0 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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:

  1. Upload volume exceeding 3x daily baseline
  2. Access to files outside normal scope
  3. Bulk downloads before resignation
  4. Off-hours file access patterns
  5. 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)

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Assess exposure of Windows secrets protected by DPAPI, the operating system’s data protection service, during authorized security tests. Guide examination and decryption of saved credentials and browser data using available user sessions, credentials, or backup keys.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Assessing saved credential exposure in authorized tests
  • Examining collected DPAPI files offline
  • Reviewing protected browser and RDP credentials
mukul975/Anthropic-Cybersecurity-Skills3.5만2026년 8월 31일 업데이트

Assess Shadow Credentials account takeover during authorized Active Directory testing. Examine permissions that allow public key registration, validate authentication without a password reset, and document credential cleanup, detection opportunities, and access control changes needed for remediation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Assessing risky permissions identified in BloodHound
  • Validating account takeover without resetting a password
  • Generating directory change records during security exercises
mukul975/Anthropic-Cybersecurity-Skills3.5만2026년 8월 31일 업데이트

Prepare for CMMC Level 2, a cybersecurity certification for defense contractors. Define the systems handling controlled information, review security requirements, calculate the SPRS assessment score, and organize remediation plans and evidence into a readiness report.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Define the assessment scope for systems handling controlled information
  • Calculate an SPRS score and prioritize unmet security requirements
  • Prepare security plans, remediation milestones, and assessment evidence
mukul975/Anthropic-Cybersecurity-Skills3.5만2026년 8월 31일 업데이트

Create disk images for investigations using dd or dcfldd on Linux. The workflow covers write protection, bit-for-bit copying, hash checks to verify integrity, and documentation of the acquisition process.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Preserving a suspect drive before investigation
  • Creating evidence copies of USB devices or memory cards
  • Verifying that a disk image matches its source
mukul975/Anthropic-Cybersecurity-Skills3.5만2026년 8월 31일 업데이트

Review Active Directory permissions with ldap3, a Python library for directory queries. Identify excessive access granted to non-administrative accounts, explain potential privilege escalation paths, and produce a JSON report with affected objects and remediation recommendations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Auditing excessive Active Directory permissions
  • Investigating privilege escalation paths from ordinary accounts
  • Reporting affected objects and permission remediation recommendations
mukul975/Anthropic-Cybersecurity-Skills3.5만2026년 8월 31일 업데이트

Inspect suspicious Android APK files without running them. Analyze permissions, app components, suspicious code patterns, and network indicators, then produce a JSON risk report to support malware triage and detection rule development.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Triage suspicious APK files without running them
  • Inspect dangerous permission combinations and suspicious API calls
  • Extract network indicators for malware detection rules
mukul975/Anthropic-Cybersecurity-Skills3.5만2026년 8월 31일 업데이트

mukul975의 스킬 모두 보기

이 스킬의 문제 신고