Enforcement Knowledge Graph for Data Security Rule Impact Analysis
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Solution Overview
Problem
Current solutions for predicting the impact of data security policy rule changes on information asset access are computationally expensive, inaccurate, and do not provide real-time analysis, failing to account for information asset access patterns and differential analysis between governance system states.
Innovation Solution
The use of an enforcement knowledge graph to identify and analyze the impact of rule changes by shredding new rules, traversing the graph to find matching information assets, and performing differential analysis between historic and predicted access patterns, reducing computational cost and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computational methods are used to predict rule change impacts, then comprehensive analysis can be performed, but computational cost becomes excessive and real-time analysis is not achieved
Solution Approach 1:
The system segments the rule space into affected and unaffected rules by identifying the new rule space boundary. This segmentation allows the system to focus computational resources only on the relevant subset of rules that are actually impacted by changes, rather than analyzing the entire rule base. The segmentation is achieved by comparing old and new rule spaces to identify differential rules, thereby reducing computational cost while maintaining prediction accuracy.
Solution Approach 2:
The system performs preliminary action by pre-computing and storing rule impact metrics in the enforcement knowledge graph before rule changes are committed. The enforcement knowledge graph pre-stores relationships between rules, information assets, and access patterns, enabling rapid lookup and differential analysis when changes occur. This preliminary structuring of data allows real-time impact prediction without performing expensive computations at the time of rule change evaluation.
2Measurement precision
If traditional computational methods are used to predict rule change impacts, then analysis can be performed, but real-time analysis capability is not achieved
Solution Approach 1:
The enforcement knowledge graph performs preliminary action by pre-computing and storing rule-information asset-user relationships before rule changes occur. This pre-computation includes building the knowledge graph structure that maps rules to affected information assets and users, storing access patterns, and organizing data for rapid differential analysis. When rule changes are proposed, the system can immediately query the pre-built graph to determine impacts without performing expensive real-time computations, thus achieving real-time analysis capability while maintaining accuracy.
Solution Approach 2:
The system creates a copy of the rule space structure in the enforcement knowledge graph, maintaining relationships between rules, information assets, and users in a query-optimized format. This copied representation allows rapid differential analysis by comparing the old rule space copy with the new rule space copy, identifying changes in access patterns without re-computing from scratch. The copying approach enables real-time analysis by providing immediate access to structured rule relationship data.
3Measurement precision
If comprehensive rule analysis is performed, then accurate impact prediction is achieved, but the system cannot balance data accessibility and compliance efficiently
Solution Approach 1:
The system segments rule changes into categories (affected vs. unaffected rules) and information assets into hot vs. cold assets based on access patterns. This segmentation enables the system to focus detailed analysis only on affected rules and hot assets that are likely to be impacted by rule changes. Unaffected rules and cold assets can be processed more quickly or skipped entirely, thereby maintaining accurate impact prediction for critical elements while improving overall rule change implementation efficiency.
Solution Approach 2:
The system replaces traditional mechanical computational analysis with a knowledge graph-based semantic analysis approach. The enforcement knowledge graph uses semantic relationships and pre-computed metrics to determine rule impacts, substituting expensive computational mechanics with efficient graph traversal and pattern matching. This substitution maintains accurate impact prediction by leveraging semantic understanding of rule relationships while dramatically improving productivity in rule change implementation through faster analysis.
Data Source
AI summary
Predicting access impact of a plurality of rule changes on a corpus of information assets is provided. A set of affected rules in a new rule space for controlling access to the corpus of information assets is received. The set of affected rules is shredded to identify right-hand side terms contained in predication blocks of the set of affected rules. An enforcement knowledge graph is traversed to identify a set of hot information assets having same terms as the right-hand side terms of the set of affected rules. The set of hot information assets having the same terms as the right-hand side terms of the set of affected rules is added to a hash table of hot information assets.