Email Risk Classification With Blocking for AI Phishing
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Solution Overview
Problem
Conventional email security protocols are ineffective against AI-generated phishing attacks, which exploit human vulnerabilities and deceive recipients by mimicking trustworthy senders, necessitating innovative detection methods.
Innovation Solution
A system and method for detecting and presenting fraudulent electronic communications using generative AI, involving a detection system that assesses risk levels, classifies communications into categories, and employs warnings and blocks interactive elements until user acknowledgment, utilizing machine learning models and interactive guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional email security protocols are used, then existing security measures are maintained, but they are ineffective against AI-generated phishing attacks
Solution Approach 1:
The system performs preliminary analysis of electronic communications by assessing risk levels and classifying messages into categories (safe, informational, required, heightened risk) before they reach the user. This advance detection and classification enables the system to prepare appropriate responses (warnings, blocking, interactive guidance) in advance, making security measures effective against AI-generated phishing attacks while maintaining compatibility with existing protocols.
2Measurement precision
If AI technology is used to detect fraudulent communications, then detection capability is improved, but system complexity increases
Solution Approach 1:
The detection system is segmented into distinct functional modules: risk level assessment, classification into risk categories, warning generation, and interactive guidance provision. This segmentation allows each component to perform a specific function with high precision while keeping the overall system manageable and maintainable, reducing the burden of complexity despite advanced AI capabilities.
3Reliability
If interactive warnings and blocking are implemented, then user protection is enhanced, but user experience may be degraded
Solution Approach 1:
The system applies different levels of intervention to different communications based on their risk classification. Safe communications pass through without any warnings or blocking, maintaining excellent user experience. Informational communications receive gentle alerts. Required risk communications trigger warnings that must be acknowledged. Heightened risk communications receive interactive guidance and may be blocked. This localized, differentiated approach ensures protection is applied precisely where needed while minimizing interference with legitimate user activities.
Data Source
AI summary
Techniques are provided for detecting and presenting fraudulent electronic communications. Electronic communication content is obtained. A risk level of the electronic communication is assessed based on the electronic communication content. The electronic communication is classified into one of a plurality of risk categories based on assessing the risk level, the plurality of risk categories comprising at least a safe risk category associated with no warnings and a required risk category. Selection of the electronic communication is detected in a communication application such that the electronic communication is at least partially displayed in a user interface of the communication application. In response to detecting selection of the electronic communication, when the electronic communication is classified in the required risk category, a required warning is displayed comprising one or more required warning elements. An interactive element of the electronic communication is blocked until the required warning is acknowledged by a user.