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.
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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.
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 โ 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
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Case ID: IT-2024-0089
Subject: [Employee Name] โ [Department]
Authorized By: [CISO / General Counsel]
Referral Source: HR โ 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 โ 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
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:
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 โ IT-2024-0089
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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) โ 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 โ 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
