Phishing Template Difficulty Calibration by User Security Maturity
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Solution Overview
Problem
Existing cybersecurity training methods struggle to effectively assess user security maturity, leading to inefficiencies in identifying and mitigating social engineering threats, as conventional tools fail to detect new and unknown threats, and employee awareness varies widely due to differing levels of exposure and cultural influences.
Innovation Solution
A method to determine the difficulty of simulated phishing templates based on user security maturity levels, involving communication of phishing simulations to users, recording responses, and calculating failure rates to adjust template difficulty accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional cybersecurity tools are used to detect threats, then known security attacks can be intercepted, but new and unknown social engineering threats cannot be detected
Solution Approach 1:
The system performs preliminary security awareness training and simulated phishing attacks before real threats occur. Users are exposed to simulated phishing scenarios in advance, allowing them to practice recognizing and responding to threats in a safe environment, thereby building defensive capabilities before encountering actual attacks
Solution Approach 2:
The system implements continuous feedback loops where user responses to simulated phishing attacks are measured, analyzed, and used to adjust future training. The phishing simulation system provides feedback on user performance, tracks security maturity changes, and adapts subsequent simulations to address specific weaknesses, creating a closed-loop learning system
2Productivity
If security awareness training is provided to all users uniformly, then basic awareness is improved, but users with different security maturities cannot be effectively differentiated
Solution Approach 1:
The system applies different training approaches and simulation difficulties to different user segments based on their security maturity levels. Instead of uniform training, users receive tailored phishing simulations matched to their specific maturity stage, with more sophisticated scenarios for advanced users and foundational scenarios for beginners, optimizing training efficiency for each group
Solution Approach 2:
The system dynamically adjusts training parameters such as simulation difficulty, frequency, and content based on measured security maturity levels. As users progress through training, the system modifies these parameters to reflect their improved capabilities, creating an adaptive training program that evolves with user development
3Reliability
If phishing simulations are made more difficult to better test users, then detection capability improves, but user frustration and false positives increase
Solution Approach 1:
The system dynamically adjusts simulation difficulty based on individual user performance and security maturity levels. Rather than using fixed difficulty levels, the phishing simulations adapt in real-time to user capabilities, increasing challenge for those who excel and providing more guidance for those who struggle, thereby maintaining optimal engagement without excessive frustration
Data Source
AI summary
Systems and methods are provided for determining template difficulty based on user security maturity. In an example, a method includes communicating one or more simulated phishing communications to a plurality of users. Each of the users are assigned a user security maturity level of a plurality of user security maturity levels. The one or more simulated phishing communications are generated using a simulated phishing template. The method includes recording the user security maturity level of a user and a type of user interaction for each of the responses to the one or more simulated phishing communications from the users and determining, a failure rate of the simulated phishing template at each user security maturity level of the plurality of user security maturity levels based on the type of user interaction for each of the responses from one or more users assigned to each user security maturity level.