SAFE Scoring Model for Cybersecurity Assessment Automation
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Solution Overview
Problem
Enterprise security decision-making is hindered by the overwhelming volume of security-related data, leading to localized and inefficient prioritization processes that consume resources and result in outdated recommendations, often requiring months to complete and lacking transparency for decision-makers with varying cybersecurity expertise.
Innovation Solution
A computerized Security Assessment For Enterprise (SAFE) Scoring Model that generates a cybersecurity model by determining Governance Policy Score, People Awareness Score, Cybersecurity Architecture Score, External Score, and Technology Score, using a mathematical scoring model to provide actionable insights quickly and transparently, accommodating expert views and reducing recommendation time through continuous feedback and machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a comprehensive security assessment process is performed manually by experts, then the quality and accuracy of security recommendations improve, but the time required increases to months and resource consumption increases
Solution Approach 1:
The patent replaces the manual mechanical process of expert analysis with an automated computerized system that uses machine learning models and algorithms to process security data, generate assessments, and provide recommendations automatically, eliminating the time-consuming manual review process while maintaining or improving assessment quality
Solution Approach 2:
The system creates a digital replica or model of the security assessment process through trained machine learning models that can replicate expert decision-making patterns, allowing multiple assessments to be performed simultaneously without requiring additional expert time
2Loss of information
If security data collection is expanded to cover the entire enterprise, then the completeness of security information improves, but the complexity of data handling and processing increases
Solution Approach 1:
The patent divides the enterprise security assessment into multiple independent scoring components (Governance Policy Score, People Awareness Score, Cybersecurity Architecture Score, External Score, and Technology Score), each handled by specialized machine learning models that process specific types of data independently, then combines them to form a comprehensive assessment
Solution Approach 2:
The system introduces an intermediary layer of machine learning models and standardized scoring frameworks that mediate between raw security data and final recommendations, automatically transforming diverse data sources into structured, comparable metrics without requiring complex manual processing
3Measurement precision
If the security assessment process is made more detailed and comprehensive, then the quality of recommendations improves, but the ease of understanding for non-expert decision-makers decreases
Solution Approach 1:
The patent employs visual indicators and standardized scoring representations that translate complex security assessments into easily interpretable formats, allowing non-expert decision-makers to quickly understand security posture and prioritization without needing deep cybersecurity knowledge
Solution Approach 2:
The system transforms complex security data into standardized numerical scores and prioritization metrics that maintain the precision of detailed analysis while presenting results in a simplified, comparable format that is accessible to decision-makers with varying levels of expertise
4Reliability
If manual prioritization processes are used to ensure thorough analysis, then the reliability of security recommendations improves, but productivity and speed of mitigation decrease
Solution Approach 1:
The patent replaces manual prioritization processes with automated machine learning models that consistently apply trained decision-making patterns to rapidly generate reliable prioritization recommendations, maintaining the rigor of expert analysis while accelerating the process from months to minutes or hours
Solution Approach 2:
The system incorporates feedback mechanisms where assessment results and outcomes are continuously fed back into the machine learning models to refine and improve recommendation reliability over time, ensuring that automated prioritization maintains or enhances the quality standards of manual processes
Data Source
AI summary
In one aspect, a computerized-process for implementing Security Assessment For Enterprise (SAFE) Scoring Model include the step of generating a cybersecurity model by the following steps. The process determines a Governance Policy Score. The process determines a People Awareness Score. The process determines a Cybersecurity Architecture Score. The process determines an External Score. The process determines a Technology Score.