Event-Driven SPI Retention for Accurate Data Purging
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Solution Overview
Problem
Current data storage systems lack mechanisms to determine when sensitive and personal information (SPI) is unlikely to be used, leading to inefficient purging, rigid retention policies, and potential non-compliance with data storage laws.
Innovation Solution
An event-based framework that learns and detects events indicating actions on user data, predicting the need for actions such as moving, modifying, or removing sensitive information, and provides real-time customer service assistance for confirmation, using reinforcement learning to improve decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If time window-based purging is used, then data retention is automated, but data may be purged prematurely when still needed
Solution Approach 1:
The system performs preliminary actions by detecting user events (such as account creation, login, data access) before making purging decisions. These events trigger predictions about future data needs, allowing the system to retain data proactively rather than reactively, preventing premature purging while maintaining automation.
Solution Approach 2:
The system incorporates feedback mechanisms where user behavior patterns and event histories continuously inform the purging decision model. By analyzing feedback from actual usage patterns, the system refines its predictions and adjusts retention decisions dynamically, improving both efficiency and accuracy over time.
2Device complexity
If time window-based purging is used, then implementation is simple, but data is stored longer than necessary
Solution Approach 1:
Instead of waiting for a time window to expire, the system performs preliminary analysis of user events and behavior patterns to predict when data will no longer be needed. This allows for more precise retention timing, reducing unnecessary storage duration while maintaining manageable system complexity through event-driven architecture.
Solution Approach 2:
The purging mechanism transitions from static time-based rules to dynamic event-driven decisions. The system continuously monitors user behavior and adjusts retention decisions in real-time based on actual usage patterns, optimizing the balance between retention accuracy and system complexity.
3Extent of automation
If time window-based purging is used, then purging is automated, but compliance with data storage laws is compromised
Solution Approach 1:
The system performs preliminary detection of user events and location changes before making purging decisions. By analyzing events such as user relocation, account status changes, and interaction patterns, the system can adapt its purging decisions to comply with varying legal requirements differentiating between user types and locations, all while maintaining automation.
Solution Approach 2:
The system changes key parameters for purging decisions based on detected user events and contextual factors. Instead of using fixed time windows, the system adjusts retention parameters dynamically based on user behavior patterns, location data, and identified compliance requirements, enabling both automation and legal compliance adaptability.
Data Source
AI summary
A system and method are disclosed for event-based data security. The method includes learning customer events which require an action on sensitive and personal information (SPI), learning a mapping between the customer events and a first subset of the SPI, detecting for a particular customer an event having an impact on the SPI of the particular customer, determining a second subset of the SPI that may be impacted by the event, determining an action to perform for the second subset of the SPI, and performing the action on the second subset of SPI. The method further includes learning the mapping using data streams, where the data streams comprise security policies, customer interactions, publicly available information and a product catalog. The method further includes where the action comprises moving the SPI, modifying the SPI, masking the SPI or removing the SPI.