AI Phishing Lure Generation for Personalized Training at Scale
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Solution Overview
Problem
Existing cybersecurity training tools face challenges in efficiently generating personalized phishing lures at scale, distinguishing between synthetic and manually generated phishing messages, and effectively detecting live phishing attacks.
Innovation Solution
A computing platform uses artificial intelligence, specifically generative pre-trained transformers (GPT2/GPT3), to generate personalized phishing lures based on personalization data, receive user feedback, and update the model, while also training an inbound message filtering system to distinguish between synthetic and manually generated phishing messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual development of phishing training messages is used, then customization and effectiveness are improved, but resource consumption and deployment scalability deteriorate
Solution Approach 1:
The system uses AI to automatically generate multiple phishing training message variants by copying and adapting patterns from real phishing messages, eliminating manual creation for each variant while maintaining customization through parameterized templates
Solution Approach 2:
The system employs parameterized templates where variables such as sender name, recipient name, company name, and message content can be dynamically changed to generate customized phishing training messages at scale without manual intervention
2Productivity
If generic phishing training messages are used, then deployment efficiency is improved, but training effectiveness deteriorates
Solution Approach 1:
The system applies local quality by inserting personalized information (recipient name, company-specific details, role-specific content) into specific locations within the phishing message template, making each message locally adapted to the target while maintaining overall deployment efficiency
3Reliability
If inbound messaging systems deflect all suspicious messages, then security is improved, but false deflection of legitimate messages increases
Solution Approach 1:
The system introduces an intermediary AI classification layer between the inbound messaging system and the deflection action, which analyzes message characteristics and confidence scores to determine whether to deflect, allowing legitimate messages to pass through while catching actual phishing attempts
4Productivity
If AI-generated synthetic phishing lures are used for training, then scalability and personalization are improved, but difficulty in distinguishing from real phishing messages increases
Solution Approach 1:
The system incorporates feedback mechanisms where users report whether they believe a phishing message is real or synthetic, and where AI models analyze linguistic patterns to identify synthetic generation artifacts, continuously improving the ability to detect and distinguish synthetic lures
Data Source
AI summary
Aspects of the disclosure relate to automated simulated phishing lure generation for cybersecurity training. The computing platform may receive personalization data. The computing platform may generate, using a phishing lure generation model, one or more simulated synthetic phishing lures based on the personalization data. The computing platform may send the one or more simulated synthetic phishing lures to one or more user devices and one or more commands directing the one or more user devices to display the one or more simulated synthetic phishing lures, which may cause the one or more user devices to display the one or more simulated synthetic phishing lures. The computing platform may receive, from the one or more user devices, feedback data corresponding to user interactions with the simulated one or more synthetic phishing lures. The computing platform may update, using the feedback data, the phishing lure generation model.