Rule-Based Criticality Scoring for Network Assets
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Solution Overview
Problem
Existing methods for assigning criticality scores to assets in a management system are manual, subjective, and labor-intensive, leading to outdated scores that do not reflect the changing security landscape.
Innovation Solution
A rule-based method for assigning criticality scores to assets using a criticality rules table that maps individual or group attributes to corresponding criticality scores, with the ability to update these rules using machine learning and operator feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to assign criticality scores to assets, then the scoring process allows for subjective evaluation and customization, but it becomes labor-intensive, expensive, and outdated quickly
Solution Approach 1:
The system enables assets to self-assess their own criticality by automatically evaluating their own attributes and comparing them against the criticality rules table. This eliminates the need for manual scoring while maintaining accuracy, as each asset independently determines its own criticality level based on current attributes.
Solution Approach 2:
The patent replaces the manual mechanical process of human evaluation with an automated computational system. The management system automatically assigns criticality scores by processing asset attributes through the criticality rules table, substituting human judgment with algorithmic evaluation that is both faster and consistently accurate.
2Productivity
If manual criticality scoring is performed infrequently to reduce labor costs, then costs are reduced, but the criticality scores become outdated and do not reflect the current security landscape
Solution Approach 1:
The system implements continuous automatic re-evaluation of asset criticality scores whenever asset attributes change. This ensures that criticality scoring is not performed infrequently but rather continuously, maintaining score relevance without incurring manual labor costs, as the automation runs on-demand based on attribute changes.
Solution Approach 2:
The management system continuously monitors asset attributes and automatically triggers re-evaluation when changes are detected. This feedback mechanism ensures that criticality scores remain current and relevant by responding to attribute changes in real-time, eliminating the need for scheduled manual updates.
3Productivity
If automated rule-based assignment is implemented, then near real-time criticality scores are achieved, but the system requires a structured rules table and machine learning components
Solution Approach 1:
The system divides the criticality assessment into discrete rules within the criticality rules table, where each rule evaluates specific asset attributes independently. This segmentation allows the complex automated system to process assets through manageable, modular rule evaluations, reducing the perceived complexity while maintaining high-speed automated scoring.
Data Source
AI summary
In an embodiment, a management system obtains a criticality rules table that includes a plurality of rules mapped to corresponding criticality scores indicative of a level of risk in the event that an associated asset of a managed network is compromised by a third party. The one embodiment, the criticality rules table is updated based upon machine learning and/or feedback from an operator of the managed network. In another embodiment, the criticality rules table is used to assign one or more criticality scores to one or more assets based on one or more attributes of one or more assets, and the criticality rules table.