Standards List

Last modified on 3 July, 2026

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Developing Organisation	Standard ID	Version	Name	Scope	Publication Date	Status	Technology Domain	Link
IEC, ISO	TR 27563	Ed. 1	Security and privacy in artificial intelligence use cases – Best practices	This document outlines best practices on assessing security and privacy in artificial intelligence use cases, covering in particular those published in ISO/IEC TR 24030. The following aspects are addressed: — an overall assessment of security and privacy on the AI system of interest; — security and privacy concerns; — security and privacy risks; — security and privacy controls; — security and privacy assurance; and — security and privacy plans. Security and privacy are treated separately as the analysis of security and the analysis of privacy can differ.	01/05/2023	Published	Artificial Intelligence, Privacy & Security	Link
ETSI	TS 133 185	14.0.0	Security aspect for LTE support of Vehicle-to-Everything (V2X) services	The present document specifies the security aspects of V2X features in LTE, including security architecture, security requirements on the network entities that are used to support V2X services, as well as the procedures and solutions which are provided to meet those requirements.	01/07/2017	Published	Privacy & Security	Link
ITU-T	X.1372	1.0	Security guidelines for vehicle-to-everything (V2X) communication	This Recommendation provides security guidelines for vehicle-to-everything (V2X) communication. V2X is a generic term comprising the communication modes termed as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-nomadic devices (V2D) and vehicle-to-pedestrian (V2P) when discussed in this Recommendation. This Recommendation identifies threats in the V2X communication environment, specifies security requirements and provides description of possible implementation of V2X communication with security. Specific security controls for V2X communication are out of scope of this Recommendation.	01/03/2020	Published	Connectivity, Privacy & Security	Link
ITU-T	X.1383	1.0	Security requirements for categorized data in vehicle-to-everything (V2X) communication	This Recommendation categorizes the data used in vehicle-to-everything (V2X) communication into several types and defines the security level for each categorized data type. Based on these categorized data types in each security level, this Recommendation provides security requirements for categorized data in V2X communication.	03/03/2023	Published	Connectivity, Privacy & Security	Link
SAE	SS_V2X_001	Ed. 1	Security Specification through the Systems Engineering Process for SAE V2X Standards	This document addresses the development of security material for application specifications in SAE V2X Technical Committees. The assumption in this document is that two groups with distinct missions contribute to the development of each standard: the “Application Specification Team is in charge of specifying the application functionality and the 'Security Specification Team' is in charge of specifying the security. The two teams may, of course, have a significant overlap of members.	01/10/2020	Published	Privacy & Security	Link
SENSORIS	SENSORIS-IA	1.5.1	SENSOR Interface Specification Interface Architecture	The Sensor Interface Specification (SENSORIS) defines an interface for requesting and sending vehicle sensor data from vehicles to clouds and across clouds. The specification and its standardization focus on the content and encoding of the interface.	14/06/2023	Published	Architecture, In-Vehicle Systems, Networks, Data and Interface Definition	Link
IEC, ISO	TR 29119-11	Ed. 1	Software and systems engineering — Software testing — Part 11: Guidelines on the testing of AI-based systems	This document provides an introduction to AI-based systems. These systems are typically complex (e.g. deep neural nets), are sometimes based on big data, can be poorly specified and can be non-deterministic, which creates new challenges and opportunities for testing them. This document explains those characteristics which are specific to AI-based systems and explains the corresponding difficulties of specifying the acceptance criteria for such systems. This document presents the challenges of testing AI-based systems, the main challenge being the test oracle problem, whereby testers find it difficult to determine expected results for testing and therefore whether tests have passed or failed. It covers testing of these systems across the life cycle and gives guidelines on how AI-based systems in general can be tested using black-box approaches and introduces white-box testing specifically for neural networks. It describes options for the test environments and test scenarios used for testing AI-based systems. In this document an AI-based system is a system that includes at least one AI component.	01/11/2020	Published	Artificial Intelligence, Testing, Verification & Validation	Link
IEC, ISO	TS 25058	Ed. 1	Software and systems engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Guidance for quality evaluation of AI systems	This document provides guidance for evaluation of artificial intelligence (AI) systems using an AI system quality model. The document is applicable to all types of organizations engaged in the development and use of AI.	01/01/2024	Published	Artificial Intelligence	Link
IEC, ISO	25059	Ed. 1	Software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Quality model for AI systems	This document outlines a quality model for AI systems and is an application-specific extension to the standards on SQuaRE. The characteristics and sub-characteristics detailed in the model provide consistent terminology for specifying, measuring and evaluating AI system quality. The characteristics and sub-characteristics detailed in the model also provide a set of quality characteristics against which stated quality requirements can be compared for completeness.	01/06/2023	Published	Artificial Intelligence	Link
CEN, CENELEC	TR 17447	Ed. 1	Space – Use of GNSS-based positioning for road Intelligent Transport System (ITS) – Mathematical PVT error model	This document is written in the frame of WP1.3 of GP-START project. It discusses several models to provide synthetic data for PVT tracks and the ways to analyse and compare the tracks to ensure these are similar to the reality.	05/02/2020	Published	Map and positioning	Link