Data Risk Index Scoring for Computing Environment Security
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Solution Overview
Problem
Existing systems lack an efficient mechanism to quantify and mitigate the security risk of data compromise in computing environments, particularly in multi-user and multi-resource scenarios.
Innovation Solution
The implementation of a data risk index (DRI) scoring system that collects resource access data to generate a DRI score indicative of the risk of resource compromise. This score is used to alert administrators and facilitate policy changes to enhance security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If permissions are granted to multiple identities to perform actions on shared resources, then operational flexibility and productivity are improved, but security risk increases due to potential unauthorized access and data compromise
Solution Approach 1:
The system dynamically changes security parameters by calculating a Data Risk Index score based on multiple factors including number of identities with access, sensitivity of data, and access patterns. This score adjusts the effective security level without restricting operational flexibility, resolving the contradiction between productivity and security reliability
Solution Approach 2:
The system implements continuous feedback by monitoring resource access data, recalculating DRI scores in real-time, and providing alerts when risk thresholds are exceeded. This feedback loop enables dynamic adjustment of security measures while maintaining operational flexibility, addressing the security-risk-productivity tradeoff
2Measurement precision
If comprehensive resource access data is collected and analyzed to calculate DRI scores, then security risk identification accuracy is improved, but computational effort and system complexity increase
Solution Approach 1:
The system segments the risk assessment process into discrete components: collecting specific resource access data points, calculating DRI scores based on defined formulas, generating alerts at threshold levels, and implementing policy changes. This segmentation improves measurement precision while managing computational complexity through modular processing
Solution Approach 2:
The system performs partial analysis by focusing on critical risk factors and using predefined DRI score thresholds to trigger alerts. This approach achieves sufficient risk identification accuracy without requiring exhaustive analysis of all possible data points, thereby reducing computational effort
Data Source
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AI summary
Methods, systems, apparatuses, and computer-readable storage mediums are described for assigning a security risk score to a resource. In one example, resource access data is collected for a resource. Based at least on the resource access data, a data risk index (DRI) score is generated for the resource. The DRI score comprises a value that is indicative of a level of risk that the resource will be compromised. At least one of the DRI score, an alert based at least on the DRI score, or a policy change for the resource based at least on the generated DRI score is reported to an administrator.