Standard Guide for Ensuring the Safety of Connected Consumer Products

Importancia y uso:

4.1 This guide is intended to apply in conjunction with applicable end-product specific performance or design standard requirements to address the overall system safety of a connected consumer product. Designers and manufacturers of a connected consumer product, with assistance from conformity assessment bodies, or others, as deemed desirable, should identify applicable and relevant product standards. They should assess and document if or how connectivity potentially affects the ability of such products to meet overall product-safety requirements, as well as conflicts or requirements, if any, that may or should not be applicable to the connected consumer product. This guide recognizes that the overall system safety of the connected consumer product is the primary objective. Property damage, including non-functionality of the connected consumer product, is only included to the extent that such property damage poses a risk of personal injury. Data security beyond the extent necessary to ensure system safety, or privacy-related issues, are not addressed in this guide although measures to address the protection of personal information may help mitigate connected consumer product safety issues.

4.2 This guide provides guidelines for:

4.2.1 Remote updates;

4.2.2 Software, firmware, and configuration risk management designed to ensure compliance with an end product safety standard and to prevent the creation of an unanticipated hazard from the connected consumer product; and

4.2.3 Cybersecurity risk controls for the software, firmware, and configuration of architecture and design.

4.2.4 The use of artificial intelligence (AI) in connected consumer products.

Subcomité:

F15.75

Referida por:

F3698-24

Volúmen:

15.11

Número ICS:

35.110 (Networking), 35.240.95 (Internet applications)

Palabras clave:

artificial intelligence; connected consumer product; connected controller; consumer product hazards; internet of things; IoT devices; machine learning;