Real-Time Language Model Coaching for Phishing and Data Leakage
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Solution Overview
Problem
Existing cybersecurity systems fail to effectively train users and recognize phishing and data leakage, phishing attacks remain a substantial cybersecurity vulnerabilities that exploit human behavior, leading to data loss and exposure.
Innovation Solution
A language model-based security tool that generates real-time notifications and trains users by capturing text from user interfaces, selecting appropriate task instructions, and using a generative language model to detect phishing or data leakage, then generates alerts and records user behavior for risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cybersecurity systems are used to detect phishing and data leakage, then basic detection capability is provided, but user training effectiveness is insufficient and detection accuracy remains low
Solution Approach 1:
The system implements feedback by analyzing user responses to phishing simulations and providing personalized training recommendations. The language model evaluates user behavior patterns and delivers targeted educational content, creating a closed-loop system where detection results directly inform training interventions, thereby improving both detection accuracy and training effectiveness simultaneously.
Solution Approach 2:
The system dynamically adjusts detection parameters and training parameters based on user behavior analysis. The language model modifies detection sensitivity and training content difficulty according to individual user risk profiles and performance metrics, enabling adaptive optimization of both detection accuracy and training effectiveness without fixed trade-offs.
2Speed
If real-time analysis of user communications is performed to detect phishing and data leakage, then detection timeliness is improved, but system complexity increases
Solution Approach 1:
The system introduces a language model as an intermediary that processes and interprets communication content. This intermediary layer enables real-time semantic analysis of emails and messages for phishing detection without requiring complex rule-based systems or extensive manual configuration, thereby achieving timely detection while managing system complexity through intelligent abstraction.
Solution Approach 2:
The language model performs self-service by autonomously analyzing communications, identifying phishing attempts, and generating training content without extensive human intervention. The system automatically adapts to new threats and user behaviors, reducing the operational complexity and maintenance burden while maintaining real-time detection capabilities.
3Reliability
If comprehensive user behavior monitoring is implemented to assess risk and provide training, then cybersecurity awareness is improved, but user privacy concerns increase
Solution Approach 1:
The system applies local quality by monitoring only the specific communication content and behavior patterns relevant to phishing and data leakage risks. Rather than comprehensive surveillance, the language model focuses analysis on localized aspects of user interactions that indicate security risks, thereby improving cybersecurity awareness while minimizing privacy intrusion by collecting only necessary information.
4Measurement precision
If language model-based detection is deployed to improve phishing and data leakage detection, then detection accuracy is enhanced, but computational resource consumption increases
Solution Approach 1:
The system applies partial action by using the language model selectively for analyzing communications that exhibit suspicious characteristics or from users with elevated risk profiles. Rather than applying full computational resources to all user communications uniformly, the system concentrates advanced language model analysis where most needed, thereby maintaining high detection accuracy while reducing overall computational resource consumption through targeted application.
Data Source
AI summary
The disclosed system generates “real-time” notifications to prevent cybersecurity violations while also effectively training users. The system captures text from a user interface and selects a set of task instructions based on whether the text corresponds to an outgoing or incoming communication. If the captured text is incoming, the system selects task instructions related to phishing. If the captured text is (intended) outgoing, then the system selects task instructions related to data leakage. The system forms a prompt with the selected task instructions and the captured text and then inputs the prompt to a generative language model. If the response from the generative language model indicates a cybersecurity violation, such as either phishing or potential data leakage, then the system generates a notification accordingly. The system also records generation of notifications per user to facilitate risk assessment.