Event-Driven Data Redaction System for Database Security
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for redacting sensitive data in large-scale storage environments are inefficient, requiring substantial computational resources and leading to delays in identifying and protecting personally identifiable and financial information, which can result in exposure to unauthorized access as the scale of data increases.
Innovation Solution
A system that generates real-time event messages for changed data objects, which are processed through message queues to update a database with facets indicating data types to be redacted, allowing immediate determination and redaction of sensitive data based on predefined rules, utilizing deep data inspection and natural language classification techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If periodic scanning of source storage systems is used to determine data for redaction, then data security is maintained, but processing time increases and productivity decreases
Solution Approach 1:
The system performs preliminary classification of data objects using event messages that are generated and processed in real-time as data is created or modified. This preliminary action identifies sensitive data before it needs to be redacted, eliminating the need for periodic full-system scanning and enabling immediate redaction when sensitive data is detected.
Solution Approach 2:
The data classification system operates autonomously by automatically generating, processing, and acting on event messages without requiring periodic external scanning operations. The system self-updates its knowledge base of sensitive data locations through continuous event processing, enabling it to service its own data security requirements in real-time.
2Measurement precision
If deep data inspection is performed on all data objects, then measurement precision of sensitive data identification improves, but computational effort and energy consumption increase
Solution Approach 1:
The system extracts only the necessary classification information from data objects through targeted event message processing rather than performing comprehensive deep inspection on all data. Event messages contain pre-extracted metadata and classification facets that identify sensitive data characteristics, allowing the system to achieve high identification accuracy without the computational overhead of full deep inspection on every data object.
Solution Approach 2:
The system applies partial deep inspection only to data objects that are identified through event messages as potentially containing sensitive information. Rather than inspecting all data objects equally, the system performs detailed analysis only where needed based on event-driven indicators, reducing overall computational effort while maintaining high precision for identifying sensitive data that requires redaction.
Data Source
AI summary
Provided are a computer program product, method, and system for determining data to redact in an object. Event messages for objects are received. Each event message includes at least one facet, each facet for an instance of data in an object resulting from deep data inspection of the object that is used to determine whether the instance of data should be redacted. The event messages are added to a message queue. Information is added to a database for each of the event messages include the at least one facet for an object in the event message. The database is queried to determine an object having a facet for an instance of data that indicates the instance of data is to be redacted. The instance of data is redacted in the determined object having the facet that indicates the instance of data is to be redacted data.