Large research facilities with continuously evolving electrical installations and many stakeholders often face unique challenges in implementing electrical safety. The Electrical Safety Project at CERN aims to standardize methods and processes in order to improve electrical risk management during maintenance and operation of the CERN accelerator complex. The project focuses on several axes for improvement. Two of these are the clear identification of the limits of responsibility along the powering supply chain among different teams that provide equipment for accelerators, and the reconstruction of the electrical dependencies between equipment from the source to the load. After introducing the project with its challenges, the paper will report on the progress made by ESP in these two specific fields and on the proposed methodology towards their long-term implementation.

With more than 30km of beam lines and over 10,000 devices, CERN's particle accelerators are rich sources of data, providing real opportunities for developing new equipment monitoring and automation tools using data analysis and machine learning. The ready availability of low-cost computers and microcontrollers, such as Raspberry Pi and ESP32 devices, could enable a flexible data acquisition system for short-term applications that do not require, or cannot justify, the development and installation of permanent acquisition infrastructure. This paper presents the initial work from a pilot project to develop such a system, and assesses its use for several applications, including speculative investigations of environmental conditions, such as temperature and high-energy hadron flux, as well as assessing the feasibility of detecting arc faults in power converters, and gathering datasets for training machine learning models. Key considerations for the implementation of this system are also discussed, including concerns around network security, data quality, data availability, hardware configurations, deployment conditions, and avoiding control system dependency, with initial recommendations given for each.

To assure all deliverables for the Long Shutdown 3 (LS3) will be met, the Accelerator Beam Transfer (ABT) group has launched a coordinated initiative to strengthen Quality Management (QM), Project Management (PM), and Enterprise Asset Management (EAM). A harmonized QM framework and enhanced Non-Conformity Report (NCR) process enable early anomaly detection and structured follow-up across design, manufacturing, installation, commissioning, and operation. The upgraded PM methods clarify project scope and responsibilities, enhance risk and milestone tracking, and support coordination across HL-LHC Project and Consolidation deliverables. In parallel, the unified EAM strategy enhances equipment traceability through standardized asset structures, linked documentation and NCRs, and it provides a comprehensive lifecycle overview. Additionally, logistics have been strengthened via a dedicated service supporting the group. Together, these measures establish a robust framework for planning, executing, and documenting ABT projects for LS3, but also future shutdowns, as well as interventions in the accelerators during operation.