Virtual Data Lake Security Platform for Real-Time Cloud Anomaly Detection
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Solution Overview
Problem
Existing cloud environments lack comprehensive security and data analytics solutions that can effectively monitor and manage compute assets, detect anomalies, and ensure compliance in real-time, while maintaining data integrity and reducing network exposure.
Innovation Solution
A data lake-enabled security platform that integrates data ingestion, processing, and user interface resources to monitor and analyze data from cloud environments, utilizing agents to collect and report information, and generate polygraphs to identify insider threats and anomalies, with data aggregation to minimize network exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive security monitoring and data analytics solutions are implemented to detect anomalies and ensure compliance, then security coverage and detection capability are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments security monitoring into specialized components: agents for data collection, polygraph generation for relationship analysis, anomaly detection for threat identification, and compliance monitoring for regulatory adherence. Each component handles specific security functions independently, reducing overall system complexity while maintaining comprehensive coverage.
Solution Approach 2:
The polygraph acts as an intermediary data structure that translates raw security data into meaningful relationships between entities. This intermediary layer simplifies the processing of complex security information by pre-establishing contextual relationships, making anomaly detection more efficient without requiring overly complex analysis systems.
2Speed
If real-time data collection and analysis from cloud environments is performed to detect anomalies, then detection speed and responsiveness are improved, but network exposure and data transmission requirements increase
Solution Approach 1:
Security agents are deployed locally within cloud environments to collect and process data near the source. This local processing reduces the volume of data that needs to be transmitted over networks, minimizing network exposure while maintaining real-time detection capability through localized polygraph generation and anomaly identification.
Solution Approach 2:
The system extracts only the essential security-relevant information from cloud environment data through agents, then processes this extracted data locally to generate polygraphs. This extraction approach filters out unnecessary data early in the pipeline, reducing network transmission requirements while preserving detection speed for critical security events.
3Object-affected harmful factors
If comprehensive data aggregation is implemented to minimize network exposure, then network security is improved, but data processing volume and computational requirements increase
Solution Approach 1:
The system performs preliminary data aggregation and polygraph generation at the data collection stage rather than waiting for comprehensive data to be gathered. By pre-processing and structuring security relationships early through agents and polygraph generators, the system reduces the computational burden on later processing stages while maintaining network security through selective data aggregation.
Solution Approach 2:
The data aggregation process is dynamic and adaptive, adjusting the level of aggregation based on security context and data importance. The system aggregates data to the necessary degree to minimize network exposure but avoids excessive aggregation that would waste computational resources, using polygraph relationships to determine when additional aggregation is necessary.
Data Source
AI summary
Methods; apparatuses, and products for providing a security platform that includes a virtual data lake, including: generating, based on security data associated with one or more monitored deployments, one or more abstracted security records, wherein the security data includes data in at least two distinct record formats; storing the one or more abstracted securing records in a virtual data lake; and generating, based on the one or more abstracted securing records, one or more security insights.