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Agent skill

Detecting Insider Data Exfiltration Via Dlp

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs.

Apache-2.0Auto-check passedData & Analytics

Install Detecting Insider Data Exfiltration Via Dlp

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-insider-data-exfiltration-via-dlp -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-insider-data-exfiltration-via-dlp --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/detecting-insider-data-exfiltration-via-dlp .claude/skills/detecting-insider-data-exfiltration-via-dlp && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
detecting-insider-data-exfiltration-via-dlp
GitHub stars
34k
Token cost
~623 tokens
SKILL.md length
137 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs.

  • Works in 5 steps: Upload volume exceeding 3x daily baseline → Access to files outside normal scope → Bulk downloads before resignation → …
  • Investigating insider threats
  • SKILL.md covers When to Use, Prerequisites, Instructions and Examples
  • Runs Python scripts from its folder

What it does

Detecting Insider Data Exfiltration Via Dlp is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. 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.

Its SKILL.md is about 620 tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in Data & Analytics, covering DataFrames. It works with pandas. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Investigating insider threats
  • Building user behavior analytics for data loss prevention

Example prompts

  • “Use the detecting-insider-data-exfiltration-via-dlp skill to detect insider data exfiltration by analyzing DLP policy violations, file access…”
  • “/detecting-insider-data-exfiltration-via-dlp”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  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

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Detecting Insider Data Exfiltration Via Dlp loads about 623 tokens when it runs, and up to ~1.1k if it reads all its reference files. Until then it costs about 94 tokens; SKILL.md has 137 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~94
When it runs · the whole SKILL.md, loaded when a task matches
~623
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 137 words, ~623 tokens.

Download SKILL.mdSave it as .claude/skills/detecting-insider-data-exfiltration-via-dlp/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
detecting-insider-data-exfiltration-via-dlp
description
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.
domain
cybersecurity
subdomain
security-operations
tags
insider-threat, data-loss-prevention, dlp, exfiltration-detection, ueba, security-operations
version
1.0
author
mahipal
license
Apache-2.0
nist_csf
DE.CM-01, RS.MA-01, GV.OV-01, DE.AE-02
mitre_attack
T1078, T1190, T1059, T1048, T1041

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.

python
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

python
# 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)

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/detecting-insider-data-exfiltration-via-dlp of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

Compare with similar skills

Detecting Insider Data Exfiltration Via Dlp next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Detecting Insider Data Exfiltration Via Dlp this skillmukul975/Anthropic-Cybersecurity-Skills34k—~623Automated safety check: PassApache-2.0
Chdb Datastorevemetric/vemetric3952 repos~1.4kAutomated safety check: PassApache-2.0
CSV Data Summarizercoffeefuelbump/csv-data-summarizer-claude-skill4682 repos~1.4kAutomated safety check: PassNone
Pandas ProJeffallan/claude-skills12k1 repos~1.5kAutomated safety check: PassMIT
Python Executorcortega26/chile-hub1132 repos~1.5kAutomated safety check: PassMIT
Retentioneering Product Analyticsretentioneering/retentioneering-tools927—~1.6kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Detecting Insider Data Exfiltration Via Dlp

What does Detecting Insider Data Exfiltration Via Dlp do?

Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs. Detecting Insider Data Exfiltration Via Dlp is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs.

When should I use Detecting Insider Data Exfiltration Via Dlp?

Detecting Insider Data Exfiltration Via Dlp fits situations like: investigating insider threats; building user behavior analytics for data loss prevention.

How do I install Detecting Insider Data Exfiltration Via Dlp in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-insider-data-exfiltration-via-dlp -a claude-code`. Or copy the skill folder (skills/detecting-insider-data-exfiltration-via-dlp in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/detecting-insider-data-exfiltration-via-dlp in your project. Claude Code loads it when a task matches its description.

How do I install Detecting Insider Data Exfiltration Via Dlp in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-insider-data-exfiltration-via-dlp -a codex`. Or copy the skill folder (skills/detecting-insider-data-exfiltration-via-dlp in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/detecting-insider-data-exfiltration-via-dlp in your project. Codex loads it when a task matches its description.

Can I use Detecting Insider Data Exfiltration Via Dlp in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-insider-data-exfiltration-via-dlp -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/detecting-insider-data-exfiltration-via-dlp, .gemini/skills/detecting-insider-data-exfiltration-via-dlp, .github/skills/detecting-insider-data-exfiltration-via-dlp and .opencode/skills/detecting-insider-data-exfiltration-via-dlp in your project.

What does Detecting Insider Data Exfiltration Via Dlp need to run?

Going by SKILL.md and its folder, Detecting Insider Data Exfiltration Via Dlp needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Detecting Insider Data Exfiltration Via Dlp access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Detecting Insider Data Exfiltration Via Dlp safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Detecting Insider Data Exfiltration Via Dlp use?

Detecting Insider Data Exfiltration Via Dlp is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Detecting Insider Data Exfiltration Via Dlp use?

About 623 tokens (SKILL.md is roughly 2.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 434 tokens, read only when the agent opens those files.

What are the alternatives to Detecting Insider Data Exfiltration Via Dlp?

Skills that share tags, products or a category with Detecting Insider Data Exfiltration Via Dlp: Chdb Datastore (vemetric/vemetric, 395 stars), CSV Data Summarizer (coffeefuelbump/csv-data-summarizer-claude-skill, 468 stars), Pandas Pro (Jeffallan/claude-skills, 12k stars) and Python Executor (cortega26/chile-hub, 113 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Detecting Insider Data Exfiltration Via Dlp?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 34,116 GitHub stars. The repository holds 644 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.