Data Source Reputation Scoring for Adversarial Environments
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Solution Overview
Problem
Current cybersecurity solutions lack the ability to effectively manage and validate data provenance in adversarial environments, leading to inadequate risk management and model errors due to insufficient contextual data and untracked inconsistencies.
Innovation Solution
A system and method utilizing a cloud computing platform with a reputation relationship graph and data quality analysis to assess data sources, providing reputation scoring and automated recommendations based on metadata and quality metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If collaborative data sets are compiled to improve risk management, then data completeness is improved, but data reliability deteriorates due to untracked inconsistencies and compromised sources
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring data quality metrics and updating reputation scores based on observed data consistency and source reliability. This closed-loop approach allows the system to adapt to changing data quality conditions and maintain reliability while accumulating collaborative data.
Solution Approach 2:
The patent introduces an intermediary reputation scoring system that acts as a mediator between multiple data sources and the risk management system. This intermediary layer validates and weights data from different sources, resolving inconsistencies before data is used for risk analysis.
2Ease of operation
If external scan information is used to characterize entities, then data collection is simplified, but measurement precision deteriorates due to lack of contextual values
Solution Approach 1:
The system merges external scan information with internal contextual data from multiple sources to create a comprehensive entity characterization. This combination preserves the simplicity of external scanning while enriching the results with contextual values from collaborative data sets.
Solution Approach 2:
The risk intelligence platform serves multiple functions: it collects external scan data, validates it against internal data, contextualizes findings, and generates comprehensive risk assessments. This multi-functional approach eliminates the need for separate systems for data collection and analysis.
3Reliability
If data quality analysis is performed on all ingested data, then data reliability is improved, but processing time increases
Solution Approach 1:
The system applies partial data quality analysis by focusing validation efforts on high-risk data sources and critical data fields. Rather than uniformly analyzing all data, the reputation scoring system prioritizes validation based on source reliability and data importance, reducing overall processing time while maintaining quality assurance.
Data Source
AI summary
Detection and mitigation of data source compromises in an adversarial information environment, featuring the ability to scan for, ingest and process, and then use relational, wide column, and graph stores for capturing entity data, their relationships, and actions associated with them. Metadata is gathered and linked to the ingested data, which provides a broader contextual view of the environment leading up to and during an event of interest. Data quality analysis is conducted as data is ingested in order to identify if a data source may be compromised. The results are used to manage the reputation of the contributing data sources.