Context-Aware Entity Engine for Secure Data Object Sharing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current techniques for identifying sensitive or confidential information in computing environments are not sufficiently accurate, leading to undetected data breaches and exfiltration attempts due to false positives and lack of context-based analysis.
Innovation Solution
An entity engine classifies content using predefined entity definitions that include regular expressions, terms, and dictionary entries, applying context-aware analysis to accurately identify sensitive information and prevent unauthorized access or exfiltration, while a validation engine ensures the integrity of these definitions by generating and verifying object identifiers and signatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simple keyword matching techniques are used to identify sensitive information, then the system is easy to implement and operate, but the accuracy is insufficient leading to false positives and missed detections
Solution Approach 1:
The patent segments the sensitive information identification process into multiple components: keyword matching, context analysis, entity recognition, and validation layers. Each component handles specific aspects of detection, allowing the system to achieve high accuracy through coordinated simple operations rather than a single complex algorithm.
Solution Approach 2:
The patent introduces intermediary elements such as context windows, entity definitions, and validation rules that mediate between simple keyword matching and final classification decisions. These intermediaries add analytical depth without requiring the core matching engine to become overly complex.
2Adaptability or versatility
If definitions for identifying sensitive information are exposed to users, then the system is easy to customize and adapt, but the definitions can be altered by malicious entities or by mistake compromising security
Solution Approach 1:
The patent implements preliminary validation and hashing of definitions before they are deployed or modified. Definitions are pre-validated against integrity constraints and stored with cryptographic hashes, ensuring that any attempted alteration can be detected before it compromises security. This allows customization while maintaining integrity through advance protective measures.
Solution Approach 2:
The patent incorporates feedback mechanisms that monitor definition usage and integrity. When definitions are accessed or modified, the system validates them against stored hashes and logs changes, providing continuous feedback on the integrity status. This enables safe customization through controlled modification processes that maintain reliability.
3Measurement precision
If context-aware analysis is implemented to reduce false positives, then the accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial context analysis by examining only relevant portions of content near detected keywords rather than analyzing entire documents. Context windows are strategically positioned around suspicious terms, providing sufficient contextual information to reduce false positives while minimizing processing overhead compared to full-document analysis.
Solution Approach 2:
The patent applies different levels of analysis quality to different parts of content based on risk assessment. High-priority keywords trigger full context analysis, while lower-priority matches receive lighter processing. This localized quality adjustment maintains accuracy for critical detections while reducing overall processing time through differentiated analysis intensity.
Data Source
AI summary
Provided herein are systems and methods for defining and securely sharing objects for use in preventing data breach or exfiltration. Memory may be configured to store a plurality of objects for use in preventing data breach or exfiltration. A validation engine can validate the objects, incorporate into each object an object identifier and a signature, and generate a subset of the objects for use by a first user. The validation engine can store, in the memory, the plurality of objects as a superset of objects corresponding to the generated subset. An evaluation engine may, responsive to identifying that one or more object identifiers and signatures in a received set of objects belong to the subset corresponding to the stored superset, verify whether any object in the received set has been tampered with.