Social Engineering Detection Playbook
Introduction: The Need for Effective Social Engineering Detection Capabilities
Social engineering attacks exploit human psychology rather than technical vulnerabilities, making them one of the most effective and persistent threats to organisations. Cybercriminals use tactics such as phishing, pretexting, baiting, and impersonation to manipulate employees, executives, and even customers into divulging sensitive information, granting unauthorised access, or executing fraudulent transactions. As attackers leverage increasingly sophisticated methods—often enhanced by artificial intelligence and deepfake technology—organisations must adopt proactive detection and prevention strategies to mitigate these threats.
Effective social engineering detection capabilities and processes are essential to identifying and mitigating manipulation-based attacks before they result in security breaches. A robust detection strategy should integrate advanced email security, anomaly-based behaviour detection, real-time identity verification, and Security Information and Event Management (SIEM) solutions to monitor for suspicious communication patterns and access anomalies. User and Entity Behavior Analytics (UEBA) and Endpoint Detection and Response (EDR) can further enhance visibility into potential social engineering threats by detecting deviations from normal user activity.
To counter social engineering risks effectively, organisations must implement continuous monitoring, automated alerts, and adaptive security controls. Security awareness training, multi-factor authentication (MFA), and well-defined incident response protocols play a crucial role in reducing the likelihood of successful attacks. By strengthening detection capabilities and response processes, businesses can better protect their employees, customers, and sensitive data from the evolving threats posed by social engineering tactics.
Table of Contents
Initial Detection of Social Engineering Attempts
Identify Suspicious Emails
Detect Malicious URL Activity
Unusual File Access Following Social Engineering Campaigns
Compromised Account Indicators
Failed Login Attempts and Account Lockouts
Logins from Unusual Locations
Unusual Privilege Elevation Attempts
Payload Delivery and Execution
Malicious Attachments Execution
Command and Control Communication Detection
Abnormal Process Execution
Threat Persistence Indicators
Persistent Email Rules Creation
OAuth Application Abuse
Credential Reuse Patterns
Incident Response and Containment
Isolate Affected Accounts and Devices
Correlate Indicators of Compromise (IoCs)
Timeline Reconstruction
Conclusion
This playbook outlines a structured methodology to detect, analyse, and respond to social engineering compromises using advanced KQL queries within Microsoft Defender and Sentinel. Each section provides multiple query options, detailed descriptions, and expected results.
1. Initial Detection of Social Engineering Attempts
Query Option 1: Identify Suspicious Emails
Description: Detects emails with suspicious subjects or domains that are frequently used in social engineering campaigns. Results provide sender and recipient details.
Query Option 2: Detect Malicious URL Activity
Description: Tracks users clicking on potentially malicious URLs, indicating interaction with phishing links. Results display users and associated URLs.
Query Option 3: Unusual File Access Following Social Engineering Campaigns
Description: Identifies users accessing sensitive files unusually, potentially due to social engineering exploitation. Results include account and device details.
2. Compromised Account Indicators
Query Option 1: Failed Login Attempts and Account Lockouts
Description: Flags accounts with repeated login failures, which may indicate password guessing or credential stuffing. Results include usernames and IPs.
Query Option 2: Logins from Unusual Locations
Description: Detects accounts logging in from unexpected geolocations. Results display user details, login locations, and associated IPs.
Query Option 3: Unusual Privilege Elevation Attempts
Description: Identifies privilege elevation commands executed by compromised accounts. Results include command details and associated accounts.
3. Payload Delivery and Execution
Query Option 1: Malicious Attachments Execution
Description: Detects execution of suspicious attachments commonly used in social engineering campaigns. Results display file execution details and associated devices.
Query Option 2: Command and Control Communication Detection
Description: Tracks devices sending significant data to public IPs, potentially indicating command and control traffic. Results include devices and IPs.
Query Option 3: Abnormal Process Execution
Description: Identifies processes spawned by email clients or documents that may indicate phishing payload execution. Results display parent processes and commands.
4. Threat Persistence Indicators
Query Option 1: Persistent Email Rules Creation
Description: Detects persistent email rules configured to forward messages externally. Results include user accounts and rule details.
Query Option 2: OAuth Application Abuse
Description: Identifies unauthorized OAuth applications approved by users. Results display app names and associated accounts.
Query Option 3: Credential Reuse Patterns
Description: Flags repeated use of tokens for sensitive accounts, potentially indicating credential abuse. Results include accounts and IPs.
5. Incident Response and Containment
Query Option 1: Isolate Affected Accounts and Devices
Description: Tracks recent activity from compromised accounts, aiding in isolation efforts. Results assist in incident containment.
Query Option 2: Correlate Indicators of Compromise (IoCs)
Description: Correlates IoCs with activities across process, network, and email events. Results highlight affected systems and artifacts.
Query Option 3: Timeline Reconstruction
Description: Creates a timeline of social engineering-related activities to provide context and incident analysis. Results display event sequences.
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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