Data Fabric Asset Inventory for Unified Attack Surface Visibility
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Solution Overview
Problem
Existing cybersecurity systems face challenges in providing a unified, comprehensive asset inventory due to fragmented data silos, leading to incomplete visibility, inefficient risk prioritization, and increased exposure to threats.
Innovation Solution
A data fabric approach integrates cybersecurity data from multiple sources using APIs and connectors to create a unified, real-time asset inventory, enabling comprehensive asset management and threat visibility through a security knowledge graph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple independent security platforms are used to manage digital assets, then specialized security functions are provided, but data silos are created that fragment asset visibility
Solution Approach 1:
The patent implements a data fabric that merges data from multiple independent security platforms into a unified asset inventory. The system collects asset data from diverse sources including endpoint detection platforms, vulnerability management platforms, cloud security platforms, and identity management platforms, then integrates this data through entity resolution processes to create a consolidated view of digital assets while maintaining the functional independence of each security platform.
Solution Approach 2:
The data fabric acts as an intermediary layer between independent security platforms and the asset management system. It provides standardized data collection interfaces that connect to various security platforms, performs entity resolution to match assets across platforms, and delivers unified asset information to security operations without requiring direct integration between the security platforms themselves.
2Loss of information
If a centralized asset inventory system is implemented to improve visibility, then comprehensive asset data can be accessed, but system complexity increases due to integrating multiple data sources
Solution Approach 1:
The system segments the complex integration task into distinct functional modules: data collection from various security platforms, entity resolution for matching assets across platforms, data standardization and enrichment, and delivery of unified asset information. This modular architecture reduces integration complexity by handling each aspect of data unification as a separate, manageable process.
Solution Approach 2:
The data fabric implements universal data collection interfaces that can connect to multiple types of security platforms through standardized protocols. The entity resolution engine provides multi-functional capability to match assets across different platform types using various identification methods, making the system adaptable to diverse data sources without requiring custom integration logic for each platform.
3Measurement precision
If asset data is collected from multiple sources to improve inventory accuracy, then comprehensive asset coverage is achieved, but data conflicts and duplications occur
Solution Approach 1:
The entity resolution engine serves as an intermediary that mediates between conflicting asset data from multiple sources. It applies matching rules and algorithms to identify when asset records from different platforms refer to the same physical or logical asset, consolidates duplicate records, and resolves attribute conflicts by prioritizing data from trusted sources or using fusion logic to determine the most accurate asset representation.
4Speed
If real-time asset monitoring is implemented to improve threat detection, then security response time is reduced, but resource consumption increases
Solution Approach 1:
The system performs preliminary entity resolution and asset consolidation in advance, creating a pre-processes unified asset inventory before security threats occur. This preliminary action organizes and cleanses asset data from multiple sources upfront, so that during security incidents, the system can quickly query the already-processed asset information without performing complex real-time data fusion, thereby reducing computational resource consumption during actual threat response.
Data Source
AI summary
The disclosed embodiments provide systems and methods for continuous exposure and attack surface management using a data fabric. Data from multiple heterogeneous cybersecurity sources, including vulnerability scanners, threat intelligence, cloud security tools, and endpoint monitoring systems, is ingested and integrated into a semantically harmonized representation, such as a security knowledge graph. This unified data model normalizes, correlates, and contextualizes diverse cybersecurity information, enabling comprehensive and real-time assessment of an organization's cybersecurity risk posture. Automated workflows trigger proactive remediation actions based on dynamically calculated exposure metrics. Additional embodiments leverage the same data fabric architecture to support specialized cybersecurity use cases, including unified vulnerability management (UVM), cyber asset attack surface management (CAASM), continuous threat exposure management (CTEM), and asset exposure management (AEM).