Adaptive Data Policy Association System
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Solution Overview
Problem
Data retention policies are often ineffective in managing data storage, leading to unneeded data being stored for extended periods, consuming resources and posing security risks, especially when the business need for data is unknown.
Innovation Solution
Associating data policies with data objects based on their access patterns, which include user interactions, access frequencies, and characteristics, to determine suitable retention and security measures, such as deletion, archival, or encryption, ensuring compliance with legal and business requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data policies are applied to retain data for extended periods, then data availability is improved, but resource consumption and security risks increase
Solution Approach 1:
The patent applies dynamics by making data retention policies adaptive rather than static. The system continuously monitors access patterns and automatically adjusts retention duration based on actual data usage. Data that is frequently accessed is retained longer, while data with declining access patterns is automatically purged, optimizing the balance between availability and resource consumption.
Solution Approach 2:
The system implements feedback mechanisms by tracking access patterns and using this information to inform retention decisions. The monitoring component continuously observes data access behavior and feeds this information back to the policy enforcement component, which adjusts retention policies accordingly. This closed-loop feedback ensures data is retained only as long as it provides business value.
2Reliability
If data retention period is extended, then data availability is improved, but security risks increase
Solution Approach 1:
The system dynamically adjusts retention periods based on real-time access pattern analysis. Instead of using fixed retention schedules, the system continuously adapts how long data is kept based on actual usage, automatically reducing retention for data that becomes obsolete. This minimizes the window of vulnerability for security risks while maintaining availability for actively used data.
Solution Approach 2:
The system systematically identifies and discards data that no longer serves a business purpose based on access pattern analysis. By automatically purging obsolete data, the system reduces the attack surface and potential targets for security breaches while recovering storage resources. The disciplined discard process ensures data is removed promptly when it ceases to be valuable.
3Measurement precision
If data access patterns are monitored to determine retention policies, then data management precision is improved, but system complexity increases
Solution Approach 1:
The system achieves multi-functionality by combining monitoring, analysis, and policy enforcement capabilities into an integrated data management platform. The same infrastructure that tracks access patterns for business intelligence purposes also drives retention policy decisions, eliminating the need for separate monitoring systems and reducing overall complexity despite the enhanced precision of data management.
Data Source
AI summary
Described are techniques for determining a data policy suitable for association with a data object based on the data access pattern for the data object. Correspondence between the data access pattern of the data object and pattern data, indicative of data access patterns stored in association with data policies, may be determined. Based on the correspondence between the data access pattern of the data object and a particular data access pattern of the pattern data, the data policy associated with the particular data access pattern may be suitable for use with the data object. A set of suitable data policies may be refined based on the content or metadata associated with the data object and the code or deployment status of services that access the data object. Once the access pattern for a data object is known, subsequent interactions with the data object may be analyzed to identify anomalous traffic.