GB/Z 265-2026Application security guidelines for ��artificial intelligence+transportation�� (English PDF)

Foreword

This document is a standard or guiding technical document.

This document complies with the provisions of GB/T 1.1-2020 "Standardization Work Guidelines Part 1.Structure and Drafting Rules of Standardization Documents".

Drafting.

Please note that some content in this document may involve patents. The issuing organization of this document assumes no responsibility for identifying patents.

This document was proposed by the Ministry of Transport and the Civil Aviation Administration of China.

This document was prepared by the National Technical Committee on Standardization of Intelligent Transportation Systems (SAC/TC 268) and the National Technical Committee on Standardization of Cybersecurity.

(SAC/TC 260) jointly manages this.

1 Scope

This document outlines the general principles and lifecycle safety elements for the safety of AI applications in transportation, and provides a typical example of AI in transportation.

Guidance and suggestions for preventing security risks in application scenarios.

2 Normative references

The contents of the following documents, through normative references within the text, constitute essential provisions of this document. Dated citations are listed below.

For references to documents, only the version corresponding to that date applies to this document; for undated references, the latest version (including all amendments) applies.

document.

GB/T 20839 General Terminology for Intelligent Transportation Systems

GB/T 25069 Information Security Technical Terminology

GB/T 41867 Terminology for Information Technology and Artificial Intelligence

GB 45438 Network Security Technology - Artificial Intelligence-Generated Synthetic Content Identification Method

3 Terms and Definitions

The terms and definitions defined in GB/T 20839, GB/T 25069 and GB/T 41867, as well as the following terms and definitions, apply to this document.

3.1

Transportation artificial intelligence application

Artificial intelligence is being used in various sectors of transportation, including highways, waterways, railways, civil aviation, postal services, urban passenger transport, and integrated transportation, as well as in government services.

Applications in business services, security supervision, and other scenarios aim to achieve expected results in areas such as enhanced retrieval, perception and cognition, reasoning and decision-making, and task scheduling.

result.

Note. This includes large-scale basic models of transportation, large-scale models of vertical sectors of transportation, and artificial intelligence applications such as intelligent agents.

3.2

Transportation Foundation Large Scale Model

This is a large model trained on a general large model, based on a high-quality dataset and corpus of general knowledge in the transportation industry.

A dataset that can be directly used to develop and train artificial intelligence models, effectively improving model performance.

3.3

Transportation-specific large-scale model

Based on the basic large model of transportation, a large model is trained on a high-quality dataset specializing in the field of transportation.

3.4

Artificial intelligence agent

Intelligent systems that can autonomously perceive their environment, make decisions, and take actions to achieve specific goals.

Note. Generally, they possess basic abilities such as memory, planning, and tool use.

END: Draft Version (GBZ265-2026)