Session Replay Privacy Masking Through Dynamic DOM Node Detection
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Solution Overview
Problem
Existing systems fail to efficiently identify and mask private information in user interactions with websites, leaving it vulnerable to unauthorized access due to dynamic changes in web document structure and the labor-intensive process of updating tags.
Innovation Solution
A system that dynamically identifies private information using session data and a detection system, updates a capture agent to mask corresponding nodes in the Document Object Model (DOM), and includes a scanning module to detect false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static identification methods are used to mask private information, then the masking module is simple to implement, but it becomes outdated as websites change and fails to protect private information effectively
Solution Approach 1:
The patent implements dynamic identification of private information by continuously monitoring user interactions and automatically updating the masking module with newly identified private information types. The system transitions from static pre-defined masks to a dynamic learning system that adapts to website changes, ensuring ongoing protection without manual intervention.
Solution Approach 2:
The masking module performs self-updates by automatically detecting new private information types through session monitoring and expanding its masking capabilities without external intervention. The system serves itself by learning from observed user interactions and autonomously improving its protection effectiveness.
2Productivity
If user interactions are monitored and stored for subsequent processing, then website issues can be identified, but private information becomes vulnerable to unauthorized access
Solution Approach 1:
The system applies masking to private information at the point of capture during session monitoring, before the data is transmitted or stored for subsequent website issue analysis. This preliminary protection ensures that even if data is stored for productivity purposes, the private information is already protected from unauthorized access.
Solution Approach 2:
The system extracts and masks private information from user interactions separately from other session data. By identifying and removing sensitive elements before storage or transmission, the system enables website issue identification while preventing unauthorized access to private information.
3Reliability
If private information is masked in all user interactions, then unauthorized access is prevented, but useful information for website analysis is lost
Solution Approach 1:
The system applies masking selectively only to identified private information types while leaving other session data unmasked. This localized approach ensures that security is maintained for sensitive data while preserving useful information for website analysis, avoiding unnecessary loss of valuable session insights.
Data Source
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AI summary
Techniques are described herein for identifying and masking instances of private information in tracked user interactions with a network site during a network session. A scanning module can receive set of captured user interactions (e.g., movements between portions of the network site and data provided in one or more fields on the network site) with the network site during the network session. Instances of private information can be identified and a number of nodes in a document object model (DOM) for the network site that correspond to each identified instance of private information. Information about each corresponding node can be provided to update a capture agent to mask each corresponding node in future user interactions.