Automated Data Protection Policy Assignment via Anonymized Analytics
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Solution Overview
Problem
Current data backup systems require significant administrative effort for initial configuration and policy assignment, especially in new IT environments and large-scale data networks, as they rely on manual or rule-based methods that are time-consuming and inefficient.
Innovation Solution
A system and method that utilizes asset metadata to automatically assign data protection policies by clustering assets based on common characteristics, leveraging anonymized analytics and user metadata to simplify policy assignment and adapt to changing environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual or rule-based methods are used for policy assignment, then policy assignment can be performed, but significant administrative effort and time are required
Solution Approach 1:
The system performs self-service by automatically discovering new assets, analyzing their metadata, and assigning appropriate protection policies without requiring manual administrative intervention. The backup system autonomously clusters assets based on common characteristics and applies policies, eliminating the time-consuming manual configuration process while maintaining accurate policy assignment.
Solution Approach 2:
The system performs preliminary action by pre-defining policy templates and asset clustering rules in advance. When new assets are discovered, the system quickly matches them against pre-established clusters and applies appropriate policies immediately, significantly reducing the time required for initial configuration and ongoing asset onboarding.
2Measurement precision
If manual policy assignment is performed, then specific policies can be assigned to assets, but the process is time-consuming and inefficient
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring asset metadata, usage patterns, and policy effectiveness. This feedback loop allows the system to automatically refine asset clustering and policy assignments, maintaining high accuracy while operating at automated speeds. The system learns from ongoing operations to improve future policy assignments.
Solution Approach 2:
The system replaces the mechanical manual process of policy assignment with an automated computational system that analyzes asset metadata, performs clustering algorithms, and applies policies automatically. This substitution eliminates manual labor while maintaining or improving assignment accuracy through consistent application of predefined rules and algorithms.
3Adaptability or versatility
If rule-based assignment is used, then automatic policy assignment can be achieved, but it requires predefined rules that may not adapt to changing environments
Solution Approach 1:
The system implements dynamic adaptability by continuously discovering new assets, analyzing their metadata, and automatically creating or updating asset clusters based on observed characteristics. When new asset types or patterns are detected, the system dynamically adjusts clustering criteria and policy assignments without requiring manual rule updates, enabling automatic adaptation to changing environments.
Solution Approach 2:
The system achieves universality by creating a flexible asset clustering framework that can handle diverse asset types and characteristics through a single unified approach. The metadata-driven clustering system universally applies to various asset categories (files, folders, databases, applications) and automatically adapts to different organizational structures and requirements without needing separate rule sets for each scenario.
Data Source
AI summary
Embodiments for a system and method of selecting data protection policies for a new system, by collecting user, policy, and asset metadata for a plurality of other users storing data dictated by one or more protection policies. The collected metadata is anonymized with respect to personal identifying information, and is stored in an anonymized analytics database. The system receives specific user, policy and asset metadata for the new system from a specific user, and matches the received specific user metadata to the collected metadata to identify an optimum protection policy of the one or more protection policies based on the assets and protection requirements of the new system. The new system is then configured with the identified optimum protection policy as an initial configuration.