Data Lake Exfiltration Detection via CnC and Destination Analysis
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Solution Overview
Problem
Existing cyber security solutions fail to effectively protect data in data lakes cloud warehouses, leading to increasing data breaches and exfiltration, despite increased investment in security measures.
Innovation Solution
Implementing a method that identifies Command and Control (CnC) channels, detects malicious compressed and encrypted data transfers, and performs destination analysis within the data store to prevent data abuse and exfiltration, using machine learning and threat intelligence to fingerprint user behavior and integrate with SIEM/SOAR systems for automated response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing cyber security tools and techniques are deployed to protect data by proxy (focusing on protecting the server/application or endpoints), then the security infrastructure is established and resources are allocated, but data breaches continue to grow and data exfiltration increases
Solution Approach 1:
The patent inverts the traditional security approach by shifting focus from protecting endpoints and servers to directly protecting data. Instead of assuming data protection through proxy security measures, the system implements data-centric security that monitors and protects data regardless of its location or access point, thereby addressing the failure of existing proxy-based security approaches
Solution Approach 2:
The patent introduces a data security platform as an intermediary layer between data and access points. This platform implements data classification, monitoring, and protection mechanisms that operate independently of traditional perimeter security, serving as a mediator that ensures data protection even when traditional security measures fail
2Reliability
If more money and resources are deployed into cyber security solutions, then the security investment increases, but data breaches continue to grow measured by any metric
Solution Approach 1:
The patent changes the fundamental parameters of security measurement and protection by implementing data classification systems that prioritize protection based on data sensitivity rather than uniform security approaches. This enables more effective allocation of security resources and implements targeted protection that prevents data exfiltration regardless of overall security investment levels
3Device complexity
If traditional proxy-based security approaches are used (protecting server/application or endpoints), then the security architecture is simplified and existing tools can be used, but they fail to effectively protect data in data lakes cloud warehouses
Solution Approach 1:
The patent implements a universal data security platform that can protect data across multiple environments (data lakes, cloud warehouses, traditional databases) using a single system. This multi-functional approach provides comprehensive data lake security without requiring separate complex security architectures for each environment, thereby improving reliability while managing complexity
Data Source
AI summary
In one aspect, a computerized method for detecting data abuse and data exfiltration in a data store or a data lakes cloud warehouse, comprising: identifying a plurality of Command and control (CnC) channels in an enterprise data cloud infrastructure; identifying and detecting malicious compressed data transfers and encrypted data transfers; implementing a destination analysis from within the data store; and implementing data abuse detection and prevention operations.