Agent skill

Investigating Insider Threat Indicators

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Investigates insider threat indicators including data exfiltration attempts, unauthorized access patterns, policy violations, and pre-departure behaviors using SIEM analytics, DLP alerts, and HR…

Apache-2.0Auto-check passedSecurity

Install Investigating Insider Threat Indicators

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill investigating-insider-threat-indicators -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills investigating-insider-threat-indicators --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/investigating-insider-threat-indicators .claude/skills/investigating-insider-threat-indicators && 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
investigating-insider-threat-indicators
GitHub stars
34k
Token cost
~2.9k tokens
SKILL.md length
463 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Investigates insider threat indicators including data exfiltration attempts, unauthorized access patterns, policy violations, and pre-departure behaviors using SIEM analytics, DLP alerts, and HR…

  • Works in 6 steps: Establish Investigation Scope and Legal… → Build Activity Timeline from SIEM → Detect Data Exfiltration Indicators → …
  • SOC teams receive insider threat referrals from HR
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder

What it does

Investigating Insider Threat Indicators is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Investigates insider threat indicators including data exfiltration attempts, unauthorized access patterns, policy violations, and pre-departure behaviors using SIEM analytics, DLP alerts, and HR data correlation. Use when SOC teams receive insider threat referrals from HR, detect anomalous data movement by employees, or need to build investigation timelines for potential insider threats.

Its SKILL.md is about 2.9k 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 Security, covering Security operations. 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

  • SOC teams receive insider threat referrals from HR
  • Detect anomalous data movement by employees
  • Need to build investigation timelines for potential insider threats

Example prompts

  • “Use the investigating-insider-threat-indicators skill to investigate insider threat indicators including data exfiltration attempts, unauthorized…”
  • “/investigating-insider-threat-indicators”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Establish Investigation Scope and Legal Authorization
  2. Build Activity Timeline from SIEM
  3. Detect Data Exfiltration Indicators
  4. Analyze Access Pattern Anomalies
  5. Correlate with HR and Physical Security Data
  6. Preserve Evidence and Document Findings

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

Investigating Insider Threat Indicators loads about 2.9k tokens when it runs, and up to ~3.5k if it reads all its reference files. Until then it costs about 108 tokens; SKILL.md has 463 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~108
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.5k

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). 463 words, ~2,929 tokens.

Download SKILL.mdSave it as .claude/skills/investigating-insider-threat-indicators/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
investigating-insider-threat-indicators
description
Investigates insider threat indicators including data exfiltration attempts, unauthorized access patterns, policy violations, and pre-departure behaviors using SIEM analytics, DLP alerts, and HR data correlation. Use when SOC teams receive insider threat referrals from HR, detect anomalous data movement by employees, or need to build investigation timelines for potential insider threats.
domain
cybersecurity
subdomain
soc-operations
tags
soc, insider-threat, data-exfiltration, dlp, ueba, investigation, hr-correlation
version
1.0
author
mahipal
license
Apache-2.0
nist_csf
DE.CM-01, DE.AE-02, RS.MA-01, DE.AE-06
mitre_attack
T1078, T1685.002, T1685.005, T1566, T1048

Investigating Insider Threat Indicators

When to Use

Use this skill when:

  • HR refers a departing employee for monitoring during their notice period
  • DLP alerts indicate bulk data downloads or transfers to personal storage
  • UEBA detects anomalous access patterns deviating significantly from peer baselines
  • Management reports concerns about an employee accessing sensitive data outside their role

Do not use without proper legal authorization — insider threat investigations must be coordinated with HR, Legal, and Privacy teams before monitoring begins.

Prerequisites

  • Legal authorization and HR referral documenting investigation justification
  • SIEM with DLP, endpoint, email, proxy, and authentication log sources
  • Data Loss Prevention (DLP) system (Microsoft Purview, Symantec, Forcepoint) with policy alerts
  • Endpoint monitoring capability (EDR with USB/removable media logging)
  • HR data feed providing employment status, notice dates, and access entitlements
  • Chain of custody procedures for evidence preservation

Workflow

Before any monitoring, ensure proper authorization:

INSIDER THREAT INVESTIGATION AUTHORIZATION
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Case ID:           IT-2024-0089
Subject:           [Employee Name] — [Department]
Authorized By:     [CISO / General Counsel]
Referral Source:   HR — Employee submitted resignation, 2-week notice
Justification:     Employee has access to trade secrets and customer PII
Scope:             Email, file access, USB, cloud storage, printing
Duration:          2024-03-15 to 2024-03-29 (notice period)
Privacy Review:    Completed — compliant with acceptable use policy
Step 2: Build Activity Timeline from SIEM

Query comprehensive activity for the subject:

spl
index=* (user="jsmith" OR src_user="jsmith" OR sender="jsmith@company.com"
         OR SubjectUserName="jsmith")
earliest="2024-03-01" latest=now
| eval event_category = case(
    sourcetype LIKE "%dlp%", "DLP",
    sourcetype LIKE "%proxy%", "Web Access",
    sourcetype LIKE "%email%", "Email",
    sourcetype LIKE "%WinEventLog%", "Endpoint",
    sourcetype LIKE "%o365%", "Cloud",
    sourcetype LIKE "%vpn%", "VPN",
    sourcetype LIKE "%badge%", "Physical Access",
    1=1, sourcetype
  )
| stats count by event_category, sourcetype, _time
| timechart span=1d count by event_category
Step 3: Detect Data Exfiltration Indicators

Bulk File Downloads (SharePoint/OneDrive):

spl
index=o365 sourcetype="o365:management:activity" Operation IN ("FileDownloaded", "FileSynced")
UserId="jsmith@company.com" earliest=-30d
| stats count AS downloads, sum(eval(if(isnotnull(FileSize), FileSize, 0))) AS total_bytes,
        dc(SourceFileName) AS unique_files
  by UserId, SiteUrl, _time
| bin _time span=1d
| eval total_gb = round(total_bytes / 1073741824, 2)
| where downloads > 50 OR total_gb > 1
| sort - total_gb

USB/Removable Media Usage:

spl
index=sysmon EventCode=1 Computer="WORKSTATION-JSMITH"
(CommandLine="*removable*" OR CommandLine="*usb*"
 OR Image="*\\xcopy*" OR Image="*\\robocopy*")
| table _time, Computer, User, Image, CommandLine
| append [
    search index=endpoint sourcetype="endpoint:device_connect"
    user="jsmith" device_type="removable"
    | table _time, user, device_name, device_serial, action
  ]
| sort _time

Email-Based Exfiltration:

spl
index=email sourcetype="o365:messageTrace"
SenderAddress="jsmith@company.com"
| eval is_external = if(match(RecipientAddress, "@company\.com$"), 0, 1)
| eval has_attachment = if(isnotnull(AttachmentName), 1, 0)
| stats count AS total_emails,
        sum(is_external) AS external_emails,
        sum(has_attachment) AS with_attachments,
        sum(eval(if(is_external=1 AND has_attachment=1, 1, 0))) AS external_with_attach,
        sum(Size) AS total_size_bytes
  by SenderAddress
| eval external_attach_pct = round(external_with_attach / total_emails * 100, 1)
| eval total_size_mb = round(total_size_bytes / 1048576, 1)

Cloud Storage Upload Detection:

spl
index=proxy user="jsmith"
(dest IN ("*dropbox.com", "*drive.google.com", "*onedrive.live.com",
          "*box.com", "*wetransfer.com", "*mega.nz")
 OR category="cloud-storage")
http_method=POST
| stats count AS uploads, sum(bytes_out) AS total_uploaded
  by user, dest, category
| eval uploaded_mb = round(total_uploaded / 1048576, 1)
| sort - uploaded_mb
Step 4: Analyze Access Pattern Anomalies

Accessing Sensitive Systems Outside Normal Scope:

spl
index=auth user="jsmith" action=success earliest=-30d
| stats dc(app) AS unique_apps, values(app) AS apps_accessed by user
| join user type=left [
    | inputlookup role_app_mapping.csv
    | search role="Financial Analyst"
    | stats values(authorized_app) AS authorized_apps by role
    | eval user="jsmith"
  ]
| eval unauthorized = mvfilter(NOT match(apps_accessed, mvjoin(authorized_apps, "|")))
| where isnotnull(unauthorized)
| table user, unauthorized, authorized_apps

After-Hours and Weekend Activity:

spl
index=* user="jsmith" earliest=-30d
| eval hour = tonumber(strftime(_time, "%H"))
| eval is_offhours = if(hour < 7 OR hour > 19, 1, 0)
| eval day = strftime(_time, "%A")
| eval is_weekend = if(day IN ("Saturday", "Sunday"), 1, 0)
| stats count AS total, sum(is_offhours) AS offhours, sum(is_weekend) AS weekend by user
| eval offhours_pct = round(offhours / total * 100, 1)
| eval weekend_pct = round(weekend / total * 100, 1)
Step 5: Correlate with HR and Physical Security Data

Compare activity to resignation timeline:

spl
| makeresults
| eval user="jsmith",
       resignation_date="2024-03-15",
       last_day="2024-03-29",
       access_revocation="2024-03-29 17:00"
| join user [
    search index=* user="jsmith" earliest=-90d
    | bin _time span=1d
    | stats count AS daily_events, dc(sourcetype) AS data_sources by user, _time
  ]
| eval phase = case(
    _time < relative_time(now(), "-30d"), "Normal (Pre-Resignation)",
    _time >= strptime(resignation_date, "%Y-%m-%d") AND _time <= strptime(last_day, "%Y-%m-%d"),
      "Notice Period",
    1=1, "Transition"
  )
| chart avg(daily_events) AS avg_events by phase

Badge/Physical Access Correlation:

spl
index=badge_access employee_id="jsmith" earliest=-30d
| stats count AS badge_events, values(door_name) AS doors_accessed,
        earliest(_time) AS first_badge, latest(_time) AS last_badge by employee_id
| eval areas = mvcount(doors_accessed)
Step 6: Preserve Evidence and Document Findings

Maintain chain of custody for all collected evidence:

python
import hashlib
import json
from datetime import datetime

evidence_log = {
    "case_id": "IT-2024-0089",
    "investigator": "soc_analyst_tier2",
    "collection_time": datetime.utcnow().isoformat(),
    "items": [
        {
            "item_id": "EV-001",
            "description": "Splunk export — all user activity 2024-03-01 to 2024-03-15",
            "file": "jsmith_activity_export.csv",
            "sha256": hashlib.sha256(open("jsmith_activity_export.csv", "rb").read()).hexdigest(),
            "collected_by": "analyst_doe",
            "collection_method": "Splunk search export"
        },
        {
            "item_id": "EV-002",
            "description": "DLP alert details — 47 policy violations",
            "file": "dlp_alerts_jsmith.json",
            "sha256": hashlib.sha256(open("dlp_alerts_jsmith.json", "rb").read()).hexdigest(),
            "collected_by": "analyst_doe",
            "collection_method": "Microsoft Purview export"
        }
    ]
}

with open(f"evidence_log_{evidence_log['case_id']}.json", "w") as f:
    json.dump(evidence_log, f, indent=2)

Key Concepts

TermDefinition
Insider ThreatRisk posed by individuals with legitimate access who misuse it for unauthorized purposes
Data ExfiltrationUnauthorized transfer of data outside the organization via email, USB, cloud, or other channels
DLPData Loss Prevention — technology monitoring and blocking unauthorized data transfers based on content policies
Notice Period MonitoringEnhanced surveillance of departing employees during their resignation-to-departure window
Chain of CustodyDocumented evidence handling procedures ensuring forensic integrity for potential legal proceedings
Need-to-Know ViolationAccessing information or systems beyond what is required for an employee's role or current tasks
Show full SKILL.md (141 more words)Show less

Tools & Systems

  • Microsoft Purview (formerly DLP): Data classification and loss prevention platform monitoring endpoints, email, and cloud storage
  • Splunk UBA: User behavior analytics detecting insider threat patterns through ML-based anomaly detection
  • Forcepoint Insider Threat: Dedicated insider threat detection platform with behavioral indicators and risk scoring
  • DTEX InTERCEPT: Endpoint-based insider threat detection focusing on user activity metadata collection
  • Code42 Incydr: Data risk detection platform specializing in file exfiltration monitoring across endpoints and cloud

Common Scenarios

  • Departing Employee: Bulk download of customer lists and product roadmaps during two-week notice period
  • Disgruntled Employee: After negative performance review, employee accesses executive salary data outside their role
  • Contractor Overreach: External consultant accessing systems beyond contracted scope, downloading source code
  • Account Misuse: Employee sharing credentials with unauthorized third party for competitive intelligence
  • Sabotage Indicator: IT admin creating backdoor accounts and modifying system configurations before departure

Output Format

INSIDER THREAT INVESTIGATION REPORT — IT-2024-0089
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Subject:      jsmith (Financial Analyst, Finance Dept)
Period:       2024-03-01 to 2024-03-15
Status:       Employee resigned 2024-03-15, last day 2024-03-29

Key Findings:
  [HIGH]  3,847 files downloaded from SharePoint (12.4 GB) — 10x peer average
  [HIGH]  USB device connected 14 times during notice period (0 times prior month)
  [HIGH]  187 emails with attachments sent to personal Gmail
  [MEDIUM] After-hours activity increased 340% during notice period
  [MEDIUM] Accessed HR salary database 3 times (not authorized for role)

Timeline:
  Mar 01-14:  Normal activity baseline (avg 150 events/day)
  Mar 15:     Resignation submitted (activity spike to 890 events)
  Mar 16-17:  Weekend access — 2,100 SharePoint downloads
  Mar 18:     USB device first connected, DLP alert triggered

Evidence Collected:   4 items (SHA-256 verified, chain of custody documented)
Recommendation:       Immediate access revocation recommended
                      Evidence package prepared for Legal review

© 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/investigating-insider-threat-indicators of mukul975/Anthropic-Cybersecurity-Skills.

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

Open the folder on GitHubat commit 54a7988

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Kubernetes Network Security Auditkubeshark/kubeshark12k—~7.3kAutomated safety check: NotesApache-2.0
Security Detection Rule Managementelastic/agent-skills5921 repos~3.9kAutomated safety check: NotesApache-2.0
Chaitin CLIchaitin/chaitin-cli115—~15kAutomated safety check: NotesGPL-3.0
GatesNebulock-Inc/agentic-threat-hunting-framework388—~12kAutomated safety check: PassMIT

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Categories

Questions about Investigating Insider Threat Indicators

What does Investigating Insider Threat Indicators do?

Investigates insider threat indicators including data exfiltration attempts, unauthorized access patterns, policy violations, and pre-departure behaviors using SIEM analytics, DLP alerts, and HR…. Investigating Insider Threat Indicators is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Investigates insider threat indicators including data exfiltration attempts, unauthorized access patterns, policy violations, and pre-departure behaviors using SIEM analytics, DLP alerts, and HR data correlation.

When should I use Investigating Insider Threat Indicators?

Investigating Insider Threat Indicators fits situations like: SOC teams receive insider threat referrals from HR; detect anomalous data movement by employees; need to build investigation timelines for potential insider threats.

How do I install Investigating Insider Threat Indicators in Claude Code?

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

How do I install Investigating Insider Threat Indicators in Codex?

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

Can I use Investigating Insider Threat Indicators 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 investigating-insider-threat-indicators -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/investigating-insider-threat-indicators, .gemini/skills/investigating-insider-threat-indicators, .github/skills/investigating-insider-threat-indicators and .opencode/skills/investigating-insider-threat-indicators in your project.

What does Investigating Insider Threat Indicators need to run?

Going by SKILL.md and its folder, Investigating Insider Threat Indicators needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Investigating Insider Threat Indicators 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 Investigating Insider Threat Indicators 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 Investigating Insider Threat Indicators use?

Investigating Insider Threat Indicators 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 Investigating Insider Threat Indicators use?

About 2.9k tokens (SKILL.md is roughly 12k 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 619 tokens, read only when the agent opens those files.

What are the alternatives to Investigating Insider Threat Indicators?

Skills that share tags, products or a category with Investigating Insider Threat Indicators: Security Alert Triage (elastic/agent-skills, 592 stars), Kubernetes Network Security Audit (kubeshark/kubeshark, 12k stars), Security Detection Rule Management (elastic/agent-skills, 592 stars) and Chaitin CLI (chaitin/chaitin-cli, 115 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Investigating Insider Threat Indicators?

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.