Co-browsing Server Sensitive Data Masking
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Solution Overview
Problem
Co-browsing systems fail to effectively prevent the exposure of sensitive information during customer interactions, as users may inadvertently enter critical data into non-sensitive fields due to errors or browser auto-fill features, leading to potential data theft and fraud.
Innovation Solution
A Critical Data Identifier and Notifier (CDIN) module is implemented on a co-browsing server to recognize patterns of sensitive data, such as account numbers and social security numbers, and temporarily mask or block such entries from being displayed to agents, while allowing non-sensitive data to be presented, with notifications to the user and agent if sensitive data is detected in incorrect fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If fields are masked to prevent exposure of sensitive information, then data security is improved, but the agent's ability to provide assistance is worsened
Solution Approach 1:
The system applies different masking policies to different fields based on their sensitivity classification. Critical fields (SSN, account numbers, passwords) are masked, while non-critical fields (address, phone number) remain visible to agents. This local differentiation allows the agent to assist customers with non-sensitive information while protecting sensitive data.
Solution Approach 2:
The co-browsing server acts as an intermediary between the customer's browser and the agent's interface. It receives data entries from the customer, applies pattern-matching algorithms to identify sensitive information, and selectively transmits only non-sensitive data to the agent. This intermediary function enables the agent to see necessary information while blocking sensitive data exposure.
2Ease of operation
If all fields are made visible to agents, then ease of operation is improved, but data security is worsened
Solution Approach 1:
The system provides feedback to agents through notifications when sensitive data patterns are detected in fields that should be masked. This feedback mechanism alerts agents to potential data exposure issues while maintaining the masking of sensitive fields, allowing agents to understand what information is visible and what is blocked.
Solution Approach 2:
The system replaces manual field-by-field visibility control with an automated pattern-matching mechanism. Instead of requiring agents to manually identify sensitive fields, the system uses algorithms to automatically detect patterns (SSN formats, account number structures, password characteristics) and applies masking accordingly, improving both security and operational efficiency.
3Object-affected harmful factors
If pattern matching is applied to detect sensitive data, then data security is improved, but device complexity is worsened
Solution Approach 1:
The system performs preliminary pattern matching and sensitivity determination before data is transmitted to the agent or stored. By pre-processing data entries through pattern-matching algorithms, the system identifies sensitive information upfront and applies masking or blocking actions, avoiding the need for complex real-time analysis during agent-customer interactions.
Solution Approach 2:
The system changes the state of data based on detected patterns - transforming sensitive data into masked representations (replacing actual values with placeholders like '***') or blocking transmission entirely. This parameter transformation approach simplifies the data handling process while maintaining security, converting complex security requirements into straightforward data transformation rules.
Data Source
AI summary
Co-browsing sessions allow an agent to see inputs of a user and assist the user in completing a task associated with the inputs, such as completing a form on an application or website. Agents may see the information provided by the user, unless that information is sensitive (e.g., social security number, account number, password, etc.), in which case the information is blocked. However, humans will make mistakes for any number of reasons. When that mistake is the providing of sensitive information in a non-sensitive field, systems and methods are provided to block such information, even when provided into a field not designated for sensitive information (e.g., city of residence). As a result, sensitive information may be entered during a co-browsing session, into a field by mistake, and not expose the information to the agent.