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
Initial Detection of Insider Abuse
Monitor Unusual File Access
Detect Suspicious Privileged Account Activity
Identify Abnormal Login Patterns
Sensitive Data Access and Exfiltration
Detect Large File Transfers
Monitor Cloud Storage Uploads
Identify Potential Data Exfiltration via Email
Privilege Escalation Indicators
Track Unusual Process Execution
Detect Privilege Escalation Attempts
Identify Abnormal Use of Admin Tools
Persistent Abuse Indicators
Monitor for Unauthorised Access Persistence
Detect Persistent Privileged User Accounts
Advanced Credential Abuse Analysis
Incident Response and Containment
Isolate Malicious Insider Activity
Correlate Indicators of Compromise (IoCs)
Timeline Reconstruction
Conclusion
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, FileAccessCountDescription: 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, LoginCountDescription: 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, LoginCountDescription: Detects logins from unexpected geolocations. Results include user accounts, login locations, and IP addresses.
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, BytesSentDescription: 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, TotalBytesSentDescription: 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, EmailCountDescription: Detects emails sent to external domains with sensitive keywords, indicating potential exfiltration. Results include sender and recipient details.
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, AccountNameDescription: 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, AccountNameDescription: 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, ToolUsageCountDescription: Tracks the use of administrative tools often leveraged for abuse. Results display devices and users.
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, LogonTypeDescription: 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, PersistentLoginsDescription: 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, IPAddressDescription: Identifies repeated token-based authentications for sensitive accounts. Results include usernames and IPs.
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, LocationDescription: 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, SHA256Description: 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 ascDescription: Creates a timeline of insider activities to provide a comprehensive view of the incident. Results show sequence and context.
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.
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