Persona-Based Security Content Delivery for Network File Systems
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Solution Overview
Problem
Conventional security event remediation techniques struggle to automatically select appropriate security content and remediation steps for users based on their personas, leading to ineffective remediation routines.
Innovation Solution
The system uses machine learning and user data from directory services, single sign-on systems, and other identity providers to determine user personas and preferences, enabling the selection and delivery of tailored security content and remediation steps through appropriate channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional security event remediation techniques are used, then security content can be delivered to users, but the content is not tailored to user personas and preferences leading to ineffective remediation
Solution Approach 1:
The system dynamically adapts security content delivery by determining user personas from user data and selecting content based on persona characteristics and preferences. The system transitions from static, one-size-fits-all content delivery to dynamic, personalized content selection that adjusts to individual user needs, thereby improving remediation effectiveness while maintaining adaptability.
Solution Approach 2:
The system changes the parameter of content personalization by incorporating user persona attributes and preferences into the content selection process. By modifying how content is selected based on user-specific parameters rather than generic delivery, the system resolves the contradiction between reliability and adaptability in security remediation.
2Productivity
If manual security remediation processes are used, then security content can be delivered, but the process is time-consuming and inefficient
Solution Approach 1:
The system implements self-service by automatically determining user personas, selecting appropriate security content, and delivering it through preferred channels without manual intervention. This automation eliminates time-consuming manual processes while maintaining high productivity in security remediation delivery.
Solution Approach 2:
The system replaces manual mechanical processes with automated machine learning-based persona determination and content selection mechanisms. This substitution of automated systems for manual operations resolves the contradiction between productivity and time loss by enabling rapid, personalized content delivery without human intervention.
3Ease of manufacture
If generic security content is delivered to all users, then implementation is simple, but the content does not address specific user needs and preferences
Solution Approach 1:
The system segments the user base into distinct personas based on user data and characteristics. By dividing users into segments with shared attributes and preferences, the system can deliver targeted content that respects user-specific information while maintaining manageable implementation through automated segmentation logic.
Data Source
AI summary
Systems and techniques for security content delivery based on tagged personas are described herein. User data may be obtained for a user of a network file system. Attributes may be extracted from the user data to establish a user persona. Event data may be obtained for a security event experienced by the network file system. The security event may be associated with the user. A set of available remediation content items may be identified using the event data. A content item may be selected from the set of remediation content items using the user persona. A transmission medium may be determined for transmission of the content item to the user using the user persona. The content item may be transmitted to the user via the transmission medium.