An AI software supply chain-oriented security evidence fusion analysis method and system

By constructing an AI software supply chain topology network and a two-stage hybrid judgment engine, the data fusion problem in the security detection of the AI ​​software supply chain in existing technologies has been solved, achieving efficient vulnerability risk assessment and automated security auditing, thereby improving the security and auditing efficiency of the AI ​​software supply chain.

CN122693017APending Publication Date: 2026-09-04HANGZHOU XIAODAO TECH CO LTD
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Patent Information

Application Number
CN202611185027.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-08-06
Publication Date
2026-09-04

AI Technical Summary

Technical Problem

In the existing security detection technology of AI software supply chain, the report fields output by various scanning tools are not standardized, the vulnerability evidence is fragmented, there is a lack of unified data fusion specifications, it is difficult to build a reliable supply chain relationship view, it is impossible to accurately locate the propagation path of vulnerabilities in the business chain, and the risk assessment lacks objective quantitative basis, resulting in low efficiency of auditing and remediation work.

Method used

Construct an AI software supply chain topology network, schedule security scanning tools to generate security evidence data, load historical vulnerability databases for high-dimensional spatial vector projection, filter risk evidence data with high semantic similarity, use a two-stage hybrid judgment engine to determine vulnerability existence and verify exploitability, calculate and sort vulnerability risk values, and output a risk priority assessment report.

Benefits of technology

It integrates multi-source security evidence, reduces the workload of manually compiling vulnerability data, significantly reduces the number of false alarms, improves the credibility of vulnerability evidence and the accuracy of security audits, automates vulnerability analysis, and improves the efficiency of security audits in the AI ​​software supply chain.

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Abstract

The application provides an AI software supply chain-oriented security evidence fusion analysis method and system, and relates to the technical field of AI software supply chain security. The method comprises receiving an AI software supply chain and extracting AI software components, and constructing an AI software supply chain topology network; performing security detection on the supply chain to generate a security scanning report, performing semantic field mapping and spatial coordinate marker alignment on the report based on the topology network, outputting security evidence data and performing high-dimensional space vector projection, calculating the semantic similarity of the security evidence data and historical empirical data, and screening risk evidence data with a semantic similarity higher than a threshold; extracting risk evidence data of the same component and importing a hybrid judgment engine to perform vulnerability existence judgment and vulnerability exploitation accessibility verification, prioritizing the risk evidence data based on vulnerability risk values, and outputting a risk priority evaluation report, thereby improving the security audit accuracy and disposal efficiency of the AI software supply chain.
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