Software Container Configuration Scoring for Supply Chain Security
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Solution Overview
Problem
Conventional software containers are susceptible to cybersecurity breaches and are often untrustworthy, posing risks to large-scale deployments, and existing efforts to secure software supply chains are sporadic and chaotic.
Innovation Solution
A system and process for securing software containers using machine-readable configuration data, involving scanning, scoring, and attestation to evaluate the security of software containers and their components, employing techniques like SquashFS and dm-verity for integrity checks and generating scores to assess risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional software containers are used for deployment, then deployment speed and ease are improved, but security reliability deteriorates due to susceptibility to breaches and corruption
Solution Approach 1:
The system performs preliminary security scanning and evaluation of software containers before deployment. Configuration data is extracted and analyzed in advance to identify security vulnerabilities, corrupted components, and potential breaches. This preliminary action allows security assessment to be completed prior to deployment, enabling fast deployment of pre-validated containers while maintaining high security reliability through proactive threat detection.
2Reliability
If security tools are added to protect software containers, then security reliability is improved, but system complexity increases
Solution Approach 1:
The security system operates autonomously by automatically extracting configuration data from software containers, scanning for vulnerabilities, and generating security evaluations without requiring manual intervention. The system self-manages the entire security assessment workflow including data extraction, analysis, and reporting, thereby improving security reliability while minimizing the increase in system complexity through automation rather than manual security processes.
3Reliability
If comprehensive security scanning is performed on all container components, then security reliability is improved, but processing time increases
Solution Approach 1:
The system extracts only the essential configuration data from software containers for security scanning, rather than analyzing all container components in detail. By taking out and focusing on critical configuration elements that most commonly contain security vulnerabilities, the system achieves comprehensive security assessment of key areas while significantly reducing processing time compared to exhaustive scanning of all container artifacts.
4Measurement precision
If configuration data is extracted and analyzed for security evaluation, then measurement precision of security risks is improved, but device complexity increases
Solution Approach 1:
The system replaces complex manual security analysis processes with automated computational methods. Configuration data is extracted and analyzed using algorithmic approaches that automatically identify security patterns, vulnerabilities, and risks. This substitution of mechanical/manual analysis with automated computational systems improves measurement precision of security assessments while managing device complexity through standardized automated procedures rather than complex human-driven processes.
Data Source
AI summary
Techniques for data security transactions using software container machine readable configuration data are described, including performing a query in an environment to request responsive data associated with a software container, identifying a software supply chain and a process associated with the software container, parsing source code of the software container and the process to identify a configuration file having machine readable code indicating whether a change has occurred to the source code when referenced to a library called by the platform configured to scan the software container and the source code, invoking, using the platform, a machine-based algorithm to analyze the machine readable code to identify configuration data associated with the change, and evaluating the configuration data to score the change, the score associated with a security tool used with the software container and applying a data transformation to the score to generate an identifier associated with the software container.