Data Platform Attack Path Scoring for Dataset Risk Mitigation
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Solution Overview
Problem
Existing data platforms struggle to effectively monitor and mitigate attack paths in cloud environments, leading to potential security vulnerabilities and compliance risks due to inadequate anomaly detection and remediation capabilities.
Innovation Solution
A data platform that includes data ingestion, processing, and user interface resources, combined with agents deployed on compute assets to collect and analyze data, generates polygraphs to identify anomalies, and provides real-time risk assessment and notification, leveraging data stores like Snowflake and user interfaces for external access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data platforms implement comprehensive anomaly detection and attack path monitoring, then security and compliance monitoring is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system segments the data processing pipeline into distinct components: data ingestion, polygraph generation, anomaly detection, and notification modules. Each component handles specific tasks independently, reducing overall system complexity while maintaining comprehensive security monitoring capabilities.
Solution Approach 2:
The patent introduces polygraphs as intermediary data structures that simplify the representation of attack paths and relationships between cloud resources. These polygraphs serve as a standardized intermediate format between raw data collection and security analysis, reducing processing complexity.
2Reliability
If real-time data collection and analysis is implemented, then anomaly detection capability is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data transformation and polygraph generation during the data ingestion phase, preparing data structures in advance for anomaly detection. This preliminary processing reduces the time required for subsequent analysis while maintaining real-time detection capability.
Solution Approach 2:
The patent employs parameter changes in data representation, transforming raw cloud data into standardized polygraph representations with optimized structures for rapid analysis. This parameter transformation enables faster anomaly detection without increasing processing time.
3Reliability
If comprehensive data collection from cloud assets is implemented, then monitoring coverage is improved, but data volume and storage requirements increase
Solution Approach 1:
The system extracts only the essential information needed for security monitoring from comprehensive cloud data collection. By focusing on extracting critical attributes for polygraph generation and anomaly detection, the system maintains broad monitoring coverage while reducing unnecessary data volume.
Solution Approach 2:
The polygraph data structure serves multiple functions simultaneously: it represents attack paths, stores resource relationships, enables anomaly detection, and supports compliance reporting. This multi-functionality reduces the need for separate data structures, thereby reducing overall data volume while maintaining comprehensive monitoring.
Data Source
AI summary
An illustrative method includes scanning a compute environment associated with an entity and identifying one or more attack paths from a network to one or more datasets associated with the entity. The one or more attack paths each include a series of risk artifacts within the compute environment that can be exploited by an attacker to access the one or more datasets. The method further includes generating one or more attack path risk scores associated with the one or more attack paths and indicative of one or more levels of risk that the one or more attack paths could be exploited to access the one or more datasets. A risk mitigation operation associated with the one or more attack paths is performed based on the one or more attack path risk scores.