Clustering Compliance Activities by Severity and Effort
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Solution Overview
Problem
Cloud-based applications face security vulnerabilities and downtime due to unperformed compliance activities, which are often assigned randomly to users, leading to increased chances of security breaches and memory overhead.
Innovation Solution
A system that uses a machine learning model to cluster compliance activities based on severity levels and levels of effort, prioritizing activities that are more likely to result in security vulnerabilities and optimizing user workloads to prevent heavy workloads and memory issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If compliance activities are assigned randomly to users, then ease of operation is improved, but security reliability deteriorates
Solution Approach 1:
The system changes the assignment parameters from random to risk-based, using risk scores and severity levels to determine which users receive which compliance activities. This transforms the assignment mechanism from simple randomness to a parameter-driven approach that considers security implications.
Solution Approach 2:
The system automatically performs the complex task of evaluating compliance activities and assigning them to appropriate users based on risk factors, eliminating the need for manual assessment. The automated system serves itself by generating and processing risk scores without human intervention.
2Measurement precision
If all compliance activities are tracked and managed individually, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system merges multiple compliance activities into risk clusters based on shared characteristics and risk factors. By grouping activities with similar risk profiles together, the system reduces the number of individual items that need to be managed separately while preserving the ability to track each activity's specific details when needed.
3Productivity
If compliance activities are prioritized by severity and effort, then productivity is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The system replaces manual evaluation of severity and effort with an automated machine learning model that calculates risk scores. This substitution transforms the mechanical process of human assessment into an automated computational system that can evaluate multiple factors simultaneously and consistently.
Data Source
AI summary
In some implementations, a compliance system may receive, from a tracking system, a set of data structures representing a set of security vulnerabilities and indicating a corresponding set of severity levels. The compliance system may determine a set of levels of effort, corresponding to remediating the set of security vulnerabilities. The compliance system may provide the set of levels of effort to a machine learning model, in order to generate a proposed change to a set of users that are responsible for remediation, and may output an indication of the proposed change. The compliance system may provide the corresponding set of severity levels and the set of levels of effort to the machine learning model in order to generate clusters for the set of security vulnerabilities. The compliance system may output, based on the clusters, an indication of which users are assigned to which security vulnerabilities.