Vulnerability Tagging for Kubernetes Workload Backup Prioritization
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Solution Overview
Problem
Current Kubernetes workload protection systems lack the ability to monitor and analyze the risk associated with each container, leading to ineffective backup prioritization based solely on application type rather than actual data vulnerability.
Innovation Solution
Implement an AI-based vulnerability tagging process that analyzes events from containers within a Kubernetes system, calculates a vulnerability score based on event severity and occurrence, and tags workloads with these scores to dynamically prioritize backups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If legacy backup applications use application-type-based prioritization, then backup policies are simple to implement, but they cannot effectively analyze and respond to actual data vulnerability levels
Solution Approach 1:
The patent introduces an AI-based vulnerability analyzer as an intermediary component that sits between the Kubernetes workload and the backup system. This analyzer processes container events, calculates vulnerability scores, and provides risk assessments to the backup system, enabling effective vulnerability-based prioritization without requiring the backup system itself to be complex
Solution Approach 2:
The patent replaces the traditional mechanical/manual approach of application-type-based prioritization with an AI-driven automated vulnerability analysis system. The AI model automatically processes container events, calculates vulnerability scores, and dynamically adjusts backup priorities, eliminating the need for manual configuration and providing more accurate risk-based protection
2Measurement precision
If backup systems monitor and analyze each container event, then vulnerability assessment accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent implements partial monitoring by focusing only on the most critical container events that actually impact data vulnerability. Rather than analyzing every possible event, the system identifies and processes only those events with significant vulnerability implications, reducing computational overhead while maintaining assessment accuracy
Solution Approach 2:
The patent transforms raw container events into standardized vulnerability score parameters through AI processing. By converting diverse event types into a unified vulnerability score metric, the system enables efficient comparison and prioritization while reducing the complexity of processing multiple different event parameters
Data Source
AI summary
A vulnerability tagging process helps prioritize backups of datasets in a network by monitoring events that affect containers in the network. The monitored events are processed by an AI-based event analyzer to characterize each event in terms of a potential for destruction or damage to the data by each event. A vulnerability measure is calculated as the product of the number of occurrences of each event based on the severity associated with each event. Once the events are analyzed and the vulnerability scores are calculated, the scores are tagged on each workload. The vulnerability tags can then be utilized by the backup server to modify protection policies and/or prioritize backup schedules for the container workloads.