Breach Detection Engine for Multi-Source Data Breach Isolation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Data breaches often go undetected for long periods due to their occurrence across multiple data sources, making timely mitigation difficult and increasing negative consequences.
Innovation Solution
A breach detection engine indexes data sources, queries using breach-related keywords, computes likelihood of breaches using weighted criteria, and isolates network nodes for remedial actions based on breach thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data breaches are monitored across multiple data sources, then detection accuracy is improved, but detection time increases and timeliness deteriorates
Solution Approach 1:
The patent segments the breach detection process into distinct phases: crawling data sources to generate indexes, querying indexes with breach keywords to produce first results, querying potential breach databases with system identity keywords to produce second results, and evaluating candidate breaches. This segmentation allows parallel processing of multiple data sources while maintaining systematic evaluation, thereby improving detection accuracy without proportionally increasing detection time.
Solution Approach 2:
The patent implements preliminary action by pre-generating indexes from crawled data sources before actual breach detection queries are executed. These indexes contain pre-processed information about data sources, making subsequent breach keyword queries and system identity queries much faster. This preliminary indexing step transforms raw data into searchable structures in advance, significantly reducing the time required for actual breach detection while maintaining comprehensive monitoring across multiple data sources.
2Reliability
If comprehensive breach evaluation criteria are used, then breach detection reliability is improved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by implementing differentiated evaluation criteria for different types of data and breach scenarios. The system assigns specific weights to various breach criteria based on their relevance to different data types (e.g., financial data, personally identifiable information, trade secrets). This allows the evaluation system to focus computational resources on the most relevant criteria for each specific breach scenario, improving reliability without requiring uniformly complex evaluation across all cases.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting breach criteria weights based on the specific context of each detected potential breach. The system can modify evaluation parameters such as sensitivity thresholds, criteria weights, and evaluation depth depending on the type of data involved, the source of the breach, and the severity indicators detected. This flexibility allows the system to maintain high reliability across diverse breach scenarios while managing computational complexity through adaptive parameter adjustment rather than fixed complex evaluation for all cases.
Data Source
AI summary
A breach detection engine detects and mitigates the effects of breaches across one or more data sources. An index is generated based on one or more data sources and the index is queried using keywords indicative of potential breaches. A database of potential breaches is populated based on the query of the index. The potential breach database is queried using keywords associated with a system identity (e.g., a third party). A likelihood of a candidate breach is identified based on a set of breach criteria weights. A network node associated with a candidate breach determined to be an actual breach is identified for isolation or for the performance of one or more additional security actions.