Remediation Backlog UI Prioritization for Security Downtime Tradeoffs
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Solution Overview
Problem
Cloud-based applications face security vulnerabilities due to incomplete compliance activities, which are often addressed in a non-optimal order, increasing the likelihood of downtime and security risks.
Innovation Solution
A system utilizing machine learning to prioritize remediation actions based on effort levels, severity, and datetimes, providing user interfaces that order and categorize these actions to facilitate more effective compliance activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If compliance activities are performed without prioritization, then all remediation actions are addressed, but security vulnerabilities persist and downtime increases due to non-optimal ordering
Solution Approach 1:
The system performs preliminary analysis of remediation actions using machine learning to determine optimal execution order before compliance activities are carried out. The backlog system assesses multiple factors including effort levels, severity, and datetimes in advance to establish a prioritized sequence, ensuring that high-impact remediation actions are performed first to maximize security improvement while minimizing downtime.
2Reliability
If machine learning prioritization is implemented, then remediation actions are optimized for security and downtime reduction, but system complexity increases
Solution Approach 1:
The patent introduces a backlog system as an intermediary layer between the tracking system and compliance execution. This mediator receives remediation actions from the tracking system, applies machine learning-based prioritization using multiple assessment factors, and outputs an optimized execution sequence. The intermediary handles the complexity of prioritization logic centrally, keeping the overall system architecture manageable while enabling sophisticated compliance optimization.
3Measurement precision
If multiple assessment factors are considered, then prioritization accuracy improves, but computational requirements and processing time increase
Solution Approach 1:
The system dynamically adjusts assessment parameters including effort levels, severity ratings, and datetimes based on the specific context of each remediation action. The machine learning model evaluates multiple parameters simultaneously but optimizes computational efficiency by weighting factors according to their relative importance for each situation, achieving high prioritization accuracy without excessive computational overhead.
Data Source
AI summary
In some implementations, an administrator device may transmit, to a backlog system, a request to assess a set of remediation actions associated with an administrator indicated in the request and a set of credentials for a tracking system and associated with the administrator. The administrator device may receive, from the backlog system, instructions for a user interface (UI) indicating the set of remediation actions in an order determined by a machine learning model. Each remediation action may be represented in the UI adjacent to a status associated with the remediation action. Additionally, or alternatively, the administrator device may receive, from the backlog system, instructions for a UI depicting the set of remediation actions relative to a set of datetimes for the set of remediation actions and a set of categories for the set of remediation actions.