Database Security Policy Optimization via Machine Learning Models
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Solution Overview
Problem
Achieving a balance between database security and performance is challenging due to the high cost of computing resources required for robust security measures, which often compromises database performance.
Innovation Solution
A computer-implemented method using machine learning to create a knowledge distillation model and a classification model that determines optimal security policy rule sets for database requests, minimizing computing resource costs while ensuring security compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robust security measures are implemented to achieve high database security, then security level is improved, but computing resource cost increases
Solution Approach 1:
The system dynamically changes security policy parameters based on learned patterns from database operations. By analyzing historical data and operation patterns, the system adjusts security check intensity and frequency, applying stricter checks only when necessary rather than uniformly across all operations, thus reducing overall computing resource consumption while maintaining security
Solution Approach 2:
The system creates simplified copies or representations of security policies that can be evaluated more efficiently. By pre-processing and caching security rule evaluations, the system avoids repeated complex computations for identical or similar database operations, reducing computing resource costs while maintaining security effectiveness
2Reliability
If comprehensive security policies are applied to all database requests, then security coverage is improved, but database performance deteriorates
Solution Approach 1:
The system applies different security check intensities to different database operations, users, or data types based on their risk profiles. High-risk operations receive comprehensive security checks, while low-risk operations receive minimal or no additional checks. This localized approach ensures adequate security coverage for critical areas while minimizing performance impact on routine operations
Solution Approach 2:
The system performs security checks selectively rather than comprehensively on all requests. By using machine learning to identify and focus security resources on suspicious or high-risk operations, the system achieves adequate security coverage with partial action, avoiding the performance penalty of applying full security scrutiny to every database request
Data Source
AI summary
A method includes: obtaining, by a database server, security policies associated with a database hosted by the database server; obtaining, by the database server, first database data associated with the database; creating, by the database server, a knowledge distillation model using the security policies and the first database data; obtaining, by the database server, second database data associated with the database; creating, by the database server, a classification model using the second database data and an output of the knowledge distillation model; receiving, by the database server, a client database request to the database; determining, by the database server, a new security policy rule set using the classification model; and applying, by the database server, the new security policy rule set to the client database request.