Interaction-Driven Security Information for Variable User Sophistication
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Solution Overview
Problem
Users of varying sophistication levels face challenges in processing large volumes of security and compliance data to efficiently monitor threats and comply with standards, as existing systems provide one-size-fits-all information that do not adapt to individual knowledge levels.
Innovation Solution
A system that determines an account knowledge level based on user interactions, tailoring responses and complexity levels to match user understanding, using both security and compliance data to provide personalized security information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If one-size-fits-all security information is provided to all users, then comprehensive security coverage is achieved, but user comprehension and engagement decrease due to varying knowledge levels
Solution Approach 1:
The system dynamically adjusts the complexity and type of security information provided based on the user's knowledge level, which is determined through analysis of their interactions. The information delivery mechanism transitions from static one-size-fits-all to dynamic adaptation, allowing the system to optimize content for each user's understanding without requiring manual configuration.
Solution Approach 2:
The system changes the parameters of information delivery by analyzing user interaction patterns (such as query complexity, response time, and engagement metrics) to infer knowledge level. Based on this inferred parameter, the system adjusts subsequent information provision parameters including detail level, technical terminology usage, and recommended actions, thereby resolving the contradiction between adaptability and system complexity.
2Loss of information
If detailed security information is provided to all users, then complete security monitoring is achieved, but processing time and cognitive load increase for users with lower sophistication
Solution Approach 1:
The system extracts and provides only the most relevant security information based on the user's knowledge level and specific needs, rather than presenting all available security data. By filtering out unnecessary complexity and focusing on actionable insights appropriate to each user's sophistication, the system maintains information completeness for high-knowledge users while reducing processing time for lower-knowledge users.
Solution Approach 2:
The system applies partial action by providing a tailored subset of security information rather than the complete set to all users. For users with lower knowledge levels, this means receiving a curated portion of security information that is most relevant and understandable to them, thereby reducing cognitive load and processing time while still addressing their security needs effectively.
3Ease of operation
If security information is simplified for less sophisticated users, then ease of understanding improves, but depth and accuracy of security insights may be reduced
Solution Approach 1:
The system applies local quality by tailoring the presentation and detail level of security information to match the specific user's knowledge level. Rather than uniformly simplifying or complicating all security information, the system adjusts the local characteristics of information delivery (such as explanation depth, terminology, and recommended actions) to each user's capabilities, thereby maintaining precision for sophisticated users while improving ease of understanding for less sophisticated users.
Data Source
AI summary
Various methods, systems, and computer program products are provided for providing personalized security information based on prompt responses. A method may include receiving a first query from a computing device associated with an account. The first query includes one or more words that are related to the account. The method may also include determining an account knowledge level for the account based on at least one of the one or more words of the first query, wherein the account knowledge level indicates a sophistication of the account. The method may further include determining a first response to the first query based on the account knowledge level associated with the account. A response complexity level of one or more words used in the first response are based on the account knowledge level.