Investigating Insider Threat Indicators

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

Step 1: Establish Investigation Scope and Legal Authorization

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:

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):

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:

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:

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:

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:

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:

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:

| 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:

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:

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

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

Source: mukul975/Anthropic-Cybersecurity-Skills → skills/investigating-insider-threat-indicators/SKILL.md