Automated Security Scanning for Virtual Machine Image Instantiation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The instantiation of virtual machine images can introduce security vulnerabilities due to the lack of secure image generation procedures and the potential for time delays between image generation and instantiation, which may not account for newly recognized security vulnerabilities.
Innovation Solution
An automated security scanning system that uses metadata attributes to select and execute security scripts for scanning incoming images, determining the necessity of a scan based on factors like scan frequency and the date of the last successful scan, thereby preventing the introduction of vulnerabilities during image instantiation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security scanning is performed on every incoming image, then security reliability is improved, but resource consumption and processing time increase
Solution Approach 1:
The system performs preliminary security scanning on images before they are stored in the container registry. This preliminary action ensures that images are pre-validated for security issues, allowing faster processing during container creation without compromising security. The security scan is executed in advance, separating the security validation step from the container instantiation step.
Solution Approach 2:
The system dynamically adjusts the security scanning process based on image attributes such as source, type, and criticality. Different images may receive different levels of scanning intensity or frequency. The system can also dynamically skip scanning for images from trusted sources or recently scanned images, optimizing the balance between security and processing speed based on real-time conditions.
2Reliability
If security scanning is performed frequently, then security reliability is improved, but resource consumption increases
Solution Approach 1:
The system implements periodic security scanning based on image attributes and risk profiles. Instead of scanning every image at the same frequency, the system schedules scans periodically based on factors such as image criticality, source trustworthiness, and time since last scan. This periodic approach ensures that high-risk images are scanned more frequently while low-risk images consume fewer resources.
Solution Approach 2:
The system changes scanning parameters such as scan depth, duration, and intensity based on image attributes. Critical images may receive more thorough scans while less critical images receive lighter scanning. The system can also adjust the scanning frequency parameter dynamically, increasing it for suspicious images and decreasing it for trusted images, thereby optimizing resource utilization while maintaining security.
3Reliability
If manual security verification is performed, then security reliability is improved, but operational complexity and time consumption increase
Solution Approach 1:
The system implements automated security scanning and verification processes that operate without manual intervention. The security engine automatically analyzes images, identifies vulnerabilities, and blocks problematic images from being used in container creation. This self-service approach maintains high security reliability while eliminating the need for manual security verification, thereby simplifying operations and reducing time consumption.
Solution Approach 2:
The system incorporates feedback mechanisms where security scan results automatically influence subsequent actions. When security issues are detected, the system automatically blocks the image and can trigger alerts or notifications. This closed-loop feedback system ensures that security decisions are made automatically based on objective criteria, maintaining reliability while avoiding manual intervention and its associated complexity.
Data Source
AI summary
Embodiments provide automated security scanning of incoming images for use in creating containers such as a Virtual Machines. Based upon attribute(s) of metadata of the incoming image, a security engine chooses from amongst stored scripts for mounting and execution by installation logic. Such scripts can relate to the scanning itself, and/or pre-scanning considerations (such as scan frequency). In one example, the meta data attribute identifies a relevant Operating System (OS) of the incoming image. Other meta data attributes such as •scan frequency, •most recent passed scan, •log information, and •contact information (for issuance of a possible alert), may also be considered as part of the processing of an incoming image. Embodiments may enhance security by avoiding introduction of vulnerabilities through image instantiation. Embodiments may also impart flexibility to conserve resources by selectively scanning according to a frequency and/or date of last successful passage of the image.