Isolated Cloud Scanner Deployment for Data Posture Analysis
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Solution Overview
Problem
Existing data posture analysis methods in cloud and on-premise environments face scalability issues, inefficiencies, and security vulnerabilities due to the need for manual deployment of scanners, which can interfere with target environments and leave data unscanned, especially in cloud-based data warehouses and SaaS applications.
Innovation Solution
A data posture analysis system that deploys scanners in a distinct scanner environment separate from the target environment, utilizing cloud computing resources for scalable, parallel scanning through containerized scanners, managed automatically without manual intervention, and leveraging serverless computing and virtual machines for efficient data store scanning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual deployment of scanners is used, then scanner customization is possible, but scalability and efficiency deteriorate due to time-consuming manual processes
Solution Approach 1:
The system enables self-service deployment where the scanner automatically provisions itself in the target cloud environment without manual intervention. The scanner uses its own credentials to authenticate and deploy, eliminating the need for manual deployment processes while maintaining customization capabilities through configurable scanner definitions.
Solution Approach 2:
Scanner definitions are pre-configured with all necessary authentication credentials, scanning parameters, and deployment instructions before execution. This preliminary preparation allows the scanner to autonomously deploy and configure itself in the target environment without requiring manual setup or intervention during deployment.
2Reliability
If scanners are deployed directly in target environments, then scanning capability is achieved, but harmful factors increase due to interference with target environments and security vulnerabilities
Solution Approach 1:
A dedicated scanner cloud environment serves as an intermediary layer between the scanner and the target cloud environments. The scanner first deploys and executes in this isolated intermediary environment, then uses the obtained scan results to assess the target environments without directly interfering with them. This mediator approach maintains scanning reliability while eliminating harmful factors such as environment interference and security vulnerabilities.
Solution Approach 2:
The system segments the scanning process into distinct phases: deployment in the scanner cloud environment, execution of scanning logic, and result generation. By separating the scanner's operational environment from the target environments being scanned, the system achieves reliable scanning capability while preventing any harmful interference with the target systems.
3Adaptability or versatility
If limited scanner deployment options are used, then deployment simplicity is maintained, but adaptability deteriorates due to inability to scan diverse cloud environments
Solution Approach 1:
The scanner cloud environment serves as a universal platform that can deploy and execute scanners against multiple types of cloud environments (AWS, Azure, GCP, on-premise) through a single unified architecture. The scanner definitions can be configured to target different cloud providers and environment types, providing multi-functionality and broad adaptability without requiring separate deployment mechanisms for each environment.
Solution Approach 2:
The system achieves adaptability to diverse cloud environments by allowing parameter changes in scanner definitions, such as modifying authentication credentials, scanning parameters, and target environment configurations. These parameter changes enable the same scanner infrastructure to adapt and scan different cloud providers and environment types without changing the fundamental deployment structure.
4Loss of information
If data stores are not scanned, then environment stability is maintained, but loss of information occurs due to un detected vulnerabilities and sensitive data
Solution Approach 1:
The system implements feedback mechanisms where scan results are generated, analyzed, and used to produce security assessments and recommendations. The feedback loop ensures that vulnerabilities and sensitive data are detected through scanning, then communicated back to users through detailed reports, enabling informed decisions about data protection and risk mitigation.
Data Source
AI summary
The technology disclosed relates to systems and methods for analyzing data posture in a computing environment. In one example, a computer-implemented method includes identifying one or more computing services in a target computing environment to scan for data posture analysis, obtaining an access permission corresponding to the one or more computing services in the target computing environment, and deploying, to a scanner cloud environment that is distinct from the target computing environment, a scanner in accordance with a scanner definition and based on the access permission corresponding to the one or more computing services. The method includes obtaining a scanner result from the scanner deployed to the scanner cloud environment. The scanner result represents a scan of storage resources in the one or more computing services in the target computing environment using the access permission. The method further includes generating a data posture analysis result based on the scanner result.