Data Confidence Fabric for Secure CI/CD Pipeline Verification
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Solution Overview
Problem
Software development and deployment processes are vulnerable to malicious attacks, compromising the integrity of applications, as malicious entities can inject malware during build and deployment, leading to increased risk for users who unknowingly install compromised software updates.
Innovation Solution
Implementing a data confidence fabric (DCF) that generates confidence scores for software development operations by associating trust information and annotations with data flowing through the pipeline, allowing infrastructure to verify the trustworthiness of applications before execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If continuous integration and continuous delivery pipelines are used to enable continuous software development and deployment, then productivity and speed of deployment are improved, but the system becomes more vulnerable to malicious attacks and compromised software integrity
Solution Approach 1:
The patent applies preliminary action by performing security analysis and generating confidence scores before software deployment. The system analyzes code, dependencies, and pipeline operations in advance to generate confidence scores that indicate the likelihood of malware presence, allowing deployment decisions to be made based on pre-evaluated trustworthiness rather than post-deployment detection
Solution Approach 2:
The patent introduces a data confidence fabric as an intermediary layer between the continuous delivery pipeline and the deployment target. This fabric generates, stores, and manages confidence scores that mediate the trust relationship, allowing the system to maintain continuous deployment while filtering out compromised software through confidence score thresholds
2Ease of operation
If customers simply accept software updates without verification to maintain ease of operation, then ease of operation is improved, but the risk of installing compromised software increases
Solution Approach 1:
The patent applies self-service by enabling the software update system to automatically generate, store, and provide confidence scores without requiring manual user verification. The system self-evaluates the trustworthiness of updates and presents confidence information to users, allowing them to make informed decisions without technical expertise or manual security checks
Solution Approach 2:
The patent implements feedback by providing confidence scores as information feedback to users during the update process. The system analyzes the update pipeline and returns confidence scores that indicate the likelihood of malware presence, allowing users to see the security assessment results and make informed decisions about whether to proceed with the update
3Reliability
If comprehensive security analysis is performed on all pipeline operations to ensure software integrity, then reliability is improved, but device complexity and analysis time increase
Solution Approach 1:
The patent applies segmentation by dividing the security analysis into distinct pipeline stages, each generating its own confidence scores for specific operations (code retrieval, compilation, testing, deployment). This modular approach allows comprehensive security verification while managing complexity through staged analysis rather than monolithic verification
Solution Approach 2:
The patent applies parameter changes by transforming security verification from a binary pass/fail approach to a continuous confidence scoring system. The system generates confidence scores based on multiple parameters (code integrity, dependency security, pipeline operation trustworthiness) and uses these scored parameters to make deployment decisions, providing granular control over security verification complexity
Data Source
AI summary
A data confidence fabric for generating data confidence scores for a build pipeline is disclosed. Confidence scores are generated for data or jobs in a build pipeline. The scores may be combined into a final confidence score that reflects a confidence in the artifact generated by the pipeline and in the pipeline. A user or infrastructure may or may not perform the artifact based on the associated confidence score.