Federated Graph Query Planning for Policy Compliance Enforcement
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Solution Overview
Problem
Existing systems lack efficient mechanisms for ensuring policy compliance when processing queries against a federated graph environment where data is stored in different data stores subject to various regional policies and regulations.
Innovation Solution
Implementing an intelligent query API and a federated query plan generating engine that uses a schema to map query components to data entities, identifies applicable policies, and enforces compliance through policy enforcement modes such as pruning or declassifying data to ensure policy adherence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in multiple disconnected data stores across different geographical locations to enable flexible query access, then query flexibility and data accessibility are improved, but determining policy compliance and data relationships becomes complex and resource intensive
Solution Approach 1:
The patent introduces a graph storage data environment as an intermediary layer between disconnected data stores and query callers. This graph environment maintains semantic connections between data from different sources, enabling flexible queries while simplifying policy compliance determination by centralizing relationship mapping in the graph layer rather than requiring complex analysis across all data stores.
Solution Approach 2:
The system segments the federated data environment into a graph storage layer that handles semantic relationships and a query execution layer that handles policy compliance. By separating these functions, the system maintains query flexibility through the graph's semantic connections while simplifying policy enforcement to operate primarily on the graph-stored data rather than analyzing all underlying data stores.
2Reliability
If comprehensive policy enforcement is implemented across all data stores to ensure compliance with data classification labels, then data security and privacy compliance are improved, but query execution time and processing resources increase
Solution Approach 1:
The patent applies preliminary action by pre-storing data classification labels and policy information in the graph storage data environment alongside the semantic relationships. When a query arrives, the system has already have the necessary compliance information readily available in the graph, eliminating the need for time-consuming real-time analysis of data classification and policy requirements across multiple data stores during query execution.
Solution Approach 2:
The system creates a copy of essential policy and classification information within the graph storage layer. Instead of querying original data stores for compliance information during query execution, the system uses the pre-copied policy and classification data stored in the graph, significantly reducing query execution time while maintaining compliance assurance.
3Measurement precision
If the system analyzes all data components to identify applicable policies and generate compliant query plans, then policy compliance accuracy is improved, but processing power and computational resources are consumed
Solution Approach 1:
The patent applies local quality by storing data classification labels and policy applicability information specifically at the relevant nodes and edges in the graph structure where they are needed. Rather than globally analyzing all data components, the system locally identifies and applies only the policies relevant to specific data entities and relationships, reducing computational overhead while maintaining compliance detection accuracy.
Data Source
AI summary
A method for implementing policy-based compliance enforcement in a federated graph data environment including receiving a query from a query caller for data stored in a graph data storage system, the query including one or more components that are mapped to data entities of the graph data storage system using a schema. The schema expresses connections between data entities of the graph data storage system and includes data classification labels for the data entities of the graph data storage system. Each of the components of the query is examined to identify components to which a policy applies, the identification being done based on the data classification labels. Upon identifying the components, selecting between multiple policy enforcement modes that include reporting violation of the policy, denying query plans in violation of the policy and generating a transformed query plan that complies with the policy. Depending upon the selection, generating the query plan and generating a report that details violation of the policy, preventing execution of the query plan and generating the report that details violation of the policy or generating a transformed query plan.