Insider Abuse Detection Playbook

Introduction: The Need for Effective Insider Abuse Detection Capabilities

Insider threats pose a unique and complex challenge to organisations, as they originate from individuals with legitimate access to systems, data, and critical assets. Insider abuse—whether intentional or unintentional—can lead to data leaks, intellectual property theft, financial fraud, and operational sabotage. Malicious insiders may exploit their access to bypass security controls, while negligent employees may inadvertently expose sensitive information through misconfigurations or weak security practices. Given the growing reliance on cloud environments, remote work, and third-party collaborations, detecting and mitigating insider threats has become more critical than ever.

Effective insider abuse detection capabilities and processes are essential to identifying suspicious activities before they escalate into security incidents. A comprehensive detection strategy should integrate user and entity behaviour analytics (UEBA), identity and access monitoring, anomaly detection, and real-time log correlation through Security Information and Event Management (SIEM) solutions. Endpoint Detection and Response (EDR) and Data Loss Prevention (DLP) tools further enhance visibility into unauthorised data access, privilege misuse, and anomalous file transfers.

To stay ahead of insider threats, organisations must implement continuous monitoring, risk-based access controls, and automated alerts tailored to insider activity. Additionally, a strong security culture with employee training, periodic access reviews, and well-defined response protocols can help mitigate risks. By strengthening detection capabilities and response processes, organisations can proactively address insider abuse, protect sensitive data, and maintain trust and operational security.

Table of Contents

1. Initial Detection of Insider Abuse

   • Monitor Unusual File Access

   • Detect Suspicious Privileged Account Activity

   • Identify Abnormal Login Patterns

2. Sensitive Data Access and Exfiltration

   • Detect Large File Transfers

   • Monitor Cloud Storage Uploads

   • Identify Potential Data Exfiltration via Email

3. Privilege Escalation Indicators

   • Track Unusual Process Execution

   • Detect Privilege Escalation Attempts

   • Identify Abnormal Use of Admin Tools

4. Persistent Abuse Indicators

   • Monitor for Unauthorised Access Persistence

   • Detect Persistent Privileged User Accounts

   • Advanced Credential Abuse Analysis

5. Incident Response and Containment

   • Isolate Malicious Insider Activity

   • Correlate Indicators of Compromise (IoCs)

   • Timeline Reconstruction

6. Conclusion

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This playbook outlines advanced techniques for detecting and analysing insider abuse across an organisation using KQL queries for Microsoft Defender and Sentinel. Each section provides multiple query options, detailed descriptions, and expected outcomes.

1. Initial Detection of Insider Abuse

Query Option 1: Monitor Unusual File Access

DeviceFileEvents
| where Timestamp > ago(24h)
| where ActionType in ("FileRead", "FileCopied", "FileDeleted")
| where FolderPath contains "Sensitive" or FolderPath contains "Confidential"
| summarize FileAccessCount = count() by DeviceName, AccountName, FolderPath
| where FileAccessCount > 10
| project DeviceName, AccountName, FolderPath, FileAccessCount

Description: Identifies unusual access to sensitive or confidential file locations. Results display user accounts, devices, and accessed file paths.

Query Option 2: Detect Suspicious Privileged Account Activity

SigninLogs
| where TimeGenerated > ago(24h)
| where UserPrincipalName endswith "<privileged_domain>"
| summarize LoginCount = count() by UserPrincipalName, IPAddress
| where LoginCount > 5
| project UserPrincipalName, IPAddress, LoginCount

Description: Tracks privileged accounts with repeated logins from the same IP, potentially indicating abuse. Results highlight accounts and IPs.

Query Option 3: Identify Abnormal Login Patterns

SigninLogs
| where TimeGenerated > ago(24h)
| where Location != "<expected_location>"
| summarize LoginCount = count() by UserPrincipalName, Location, IPAddress
| where LoginCount > 1
| project UserPrincipalName, Location, IPAddress, LoginCount

Description: Detects logins from unexpected geolocations. Results include user accounts, login locations, and IP addresses.

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2. Sensitive Data Access and Exfiltration

Query Option 1: Detect Large File Transfers

DeviceNetworkEvents
| where Timestamp > ago(24h)
| where BytesSent > 10000000
| project Timestamp, DeviceName, RemoteIPAddress, BytesSent

Description: Flags large outbound data transfers that could indicate data exfiltration. Results display devices and remote IPs.

Query Option 2: Monitor Cloud Storage Uploads

DeviceNetworkEvents
| where Timestamp > ago(24h)
| where RemoteDnsDomain contains_any ("amazonaws.com", "blob.core.windows.net", "googleapis.com")
| summarize TotalBytesSent = sum(BytesSent) by DeviceName, RemoteDnsDomain
| where TotalBytesSent > 5000000
| project DeviceName, RemoteDnsDomain, TotalBytesSent

Description: Tracks significant data uploads to cloud storage services. Results highlight devices, domains, and data volumes.

Query Option 3: Identify Potential Data Exfiltration via Email

EmailEvents
| where Timestamp > ago(24h)
| where RecipientDomain != "<organization_domain>" and SenderEmailAddress contains "<sensitive_keywords>"
| summarize EmailCount = count() by SenderEmailAddress, RecipientDomain
| where EmailCount > 5
| project SenderEmailAddress, RecipientDomain, EmailCount

Description: Detects emails sent to external domains with sensitive keywords, indicating potential exfiltration. Results include sender and recipient details.

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3. Privilege Escalation Indicators

Query Option 1: Track Unusual Process Execution

DeviceProcessEvents
| where Timestamp > ago(24h)
| where ProcessCommandLine contains_any ("whoami", "net user", "nltest")
| project Timestamp, DeviceName, ProcessCommandLine, AccountName

Description: Flags commands commonly used to enumerate accounts and privileges. Results include the device and account executing the commands.

Query Option 2: Detect Privilege Escalation Attempts

DeviceProcessEvents
| where Timestamp > ago(7d)
| where ProcessCommandLine matches regex @"(\\bexploit\\b|\\belevate\\b|\\bprivilege\\b)"
| project Timestamp, DeviceName, ProcessCommandLine, AccountName

Description: Identifies commands potentially used for privilege escalation. Results highlight timestamps, accounts, and associated devices.

Query Option 3: Identify Abnormal Use of Admin Tools

DeviceProcessEvents
| where Timestamp > ago(7d)
| where FileName in ("psexec.exe", "wmic.exe")
| summarize ToolUsageCount = count() by DeviceName, AccountName, FileName
| where ToolUsageCount > 3
| project DeviceName, AccountName, FileName, ToolUsageCount

Description: Tracks the use of administrative tools often leveraged for abuse. Results display devices and users.

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4. Persistent Abuse Indicators

Query Option 1: Monitor for Unauthorized Access Persistence

DeviceLogonEvents
| where Timestamp > ago(24h)
| where LogonType == "TokenBased" and AccountName != "<authorized_accounts>"
| project Timestamp, DeviceName, AccountName, LogonType

Description: Detects persistent access via token-based authentication for unauthorized accounts. Results include accounts and devices.

Query Option 2: Detect Persistent Privileged User Accounts

SigninLogs
| where TimeGenerated > ago(7d)
| where UserPrincipalName endswith "<privileged_domain>"
| summarize PersistentLogins = count() by UserPrincipalName
| where PersistentLogins > 10
| project UserPrincipalName, PersistentLogins

Description: Flags privileged accounts with unusually high login activity. Results display account names and login counts.

Query Option 3: Advanced Credential Abuse Analysis

SigninLogs
| where TimeGenerated > ago(7d)
| where AuthenticationMethod contains "Token" and UserPrincipalName in ("<sensitive_accounts>")
| project Timestamp, UserPrincipalName, AuthenticationMethod, IPAddress

Description: Identifies repeated token-based authentications for sensitive accounts. Results include usernames and IPs.

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5. Incident Response and Containment

Query Option 1: Isolate Malicious Insider Activity

SigninLogs
| where TimeGenerated > ago(24h)
| where UserPrincipalName in ("<compromised_accounts>")
| project Timestamp, UserPrincipalName, IPAddress, Location

Description: Tracks recent activity for compromised accounts. Results assist in isolating the insider's activity.

Query Option 2: Correlate Indicators of Compromise (IoCs)

union DeviceProcessEvents, DeviceFileEvents, DeviceNetworkEvents
| where SHA256 in ("<IoC_hashes>")
| project Timestamp, EventType = $table, DeviceName, FileName, SHA256

Description: Correlates IoCs with device activities. Results display impacted devices and file details.

Query Option 3: Timeline Reconstruction

union DeviceProcessEvents, DeviceNetworkEvents, DeviceFileEvents
| where Timestamp > ago(30d)
| project Timestamp, EventType = $table, DeviceName, ProcessCommandLine, RemoteIPAddress, FileName
| order by Timestamp asc

Description: Creates a timeline of insider activities to provide a comprehensive view of the incident. Results show sequence and context.

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6. Conclusion

The playbook offers a good approach to detecting and analysing compromises in an environment. However, its usefulness depends on the environment and tools at your disposal. For an environment where KQL is an option, the queries may require some adaptation to specific data sources and infrastructure setup.