Customer Phishing Detection Playbook
Introduction: The Need for Effective Customer Phishing Detection Capabilities
Customer phishing attacks pose a significant threat to businesses, targeting customers with fraudulent emails, fake websites, and impersonation schemes designed to steal credentials, financial information, or sensitive personal data. Cybercriminals exploit trust in well-known brands by creating convincing phishing campaigns that mimic legitimate communications, leading to account takeovers, financial fraud, and reputational damage for organisations. As phishing tactics grow more sophisticated—leveraging AI-generated emails, brand spoofing, and advanced social engineering techniques—businesses must implement proactive detection capabilities to protect their customers and brand integrity.
Effective customer phishing detection capabilities and processes are essential for identifying and mitigating phishing campaigns before they cause widespread harm. A robust detection strategy should include brand monitoring, domain spoofing detection, real-time threat intelligence, and machine learning-based anomaly detection to identify fraudulent emails, websites, and social media scams. Security solutions such as DMARC (Domain-based Message Authentication, Reporting & Conformance), AI-driven email filtering, and Security Information and Event Management (SIEM) platforms help enhance visibility into phishing threats targeting customers.
To combat customer phishing effectively, organisations must implement continuous monitoring, automated alerting, and rapid response mechanisms, including takedown services for fraudulent domains. Additionally, proactive customer education and awareness initiatives can help mitigate the risks of phishing scams. By strengthening detection capabilities and response processes, businesses can protect their customers, reduce fraud-related losses, and maintain trust in their brand.
Table of Contents
Initial Detection of Phishing Campaign
Identify Suspicious Emails Targeting Customers
Detect Malicious URL Activity
Analyse Unusual Traffic from Customer Accounts
Compromised Customer Account Indicators
Failed Login Attempts
Unusual Login Patterns
Email Forwarding or Auto-Reply Rules
Threat Delivery and Payload Analysis
Malicious Attachments
URL Redirect Chains
Advanced Payload Execution Monitoring
Threat Persistence
Monitoring for Persistent Phishing Rules
OAuth Application Exploitation
Indicators of Repeated Credential Abuse
Incident Response and Containment
Isolate Affected Accounts and Devices
Correlate Indicators of Compromise (IoCs)
Timeline Reconstruction
Conclusion
This playbook provides a structured approach to detecting and investigating customer phishing compromises within an organisation using KQL queries with Microsoft Defender and Sentinel. Each section contains multiple query options, detailed descriptions, and expected results.
1. Initial Detection of Phishing Campaign
Query Option 1: Identify Suspicious Emails Targeting Customers
Description: Detects phishing emails targeting customers by analysing suspicious subjects and sender details. Results include email headers and sender IPs.
Query Option 2: Detect Malicious URL Activity
Description: Tracks customers clicking on malicious URLs multiple times. Results include recipient email addresses and associated URLs.
Query Option 3: Analyse Unusual Traffic from Customer Accounts
Description: Identifies devices with high volumes of outbound traffic to public IPs, potentially communicating with phishing infrastructure. Results display affected devices and IPs.
2. Compromised Customer Account Indicators
Query Option 1: Failed Login Attempts
Description: Detects customers with repeated failed login attempts, possibly due to credential stuffing or phishing. Results show usernames and IP addresses.
Query Option 2: Unusual Login Patterns
Description: Flags logins from unexpected geolocations. Results include account names, locations, and associated IPs.
Query Option 3: Email Forwarding or Auto-Reply Rules
Description: Detects the creation of email rules that forward emails externally, a common indicator of compromised accounts. Results display affected accounts and rule details.
3. Threat Delivery and Payload Analysis
Query Option 1: Malicious Attachments
Description: Flags suspicious attachments often used in phishing campaigns. Results show filenames and associated senders.
Query Option 2: URL Redirect Chains
Description: Maps URL redirect chains to identify phishing paths. Results include recipient emails and redirect URLs.
Query Option 3: Advanced Payload Execution Monitoring
Description: Identifies processes spawned by email clients or documents, indicating possible phishing payload execution. Results display command details and parent processes.
4. Threat Persistence
Query Option 1: Monitoring for Persistent Phishing Rules
Description: Detects persistent email rules created in customer accounts. Results include rule details and affected users.
Query Option 2: OAuth Application Exploitation
Description: Identifies unauthorized OAuth application approvals. Results include application names and associated accounts.
Query Option 3: Indicators of Repeated Credential Abuse
Description: Flags repeated token-based authentication attempts for sensitive customer accounts. Results include usernames and IPs.
5. Incident Response and Containment
Query Option 1: Isolate Affected Accounts and Devices
Description: Tracks activity from known compromised accounts. Results assist in isolating accounts.
Query Option 2: Correlate Indicators of Compromise (IoCs)
Description: Correlates IoCs with processes, email, and network activities. Results highlight impacted devices and files.
Query Option 3: Timeline Reconstruction
Description: Creates a timeline of phishing-related 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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