AI & ML
A Cross-Domain TARA-to-NAC Rule Mapping Methodology: From Automotive OEM Networks to Energy Storage Systems
arXiv eess.SY0 views
arXiv:2611.09771v1 Announce Type: cross Abstract: Threat analysis and risk assessment under ISO/SAE 21434 ends with cybersecurity goals, but existing methods give little structured guidance for turning those goals into enforceable network access control rules. Compliance standards such as IEC 62443, ISO/SAE 21434, and ISO/IEC 27001 are usually applied as parallel efforts rather than as one enforcement architecture. This paper presents a six-step methodology that maps cybersecurity goals from a threat analysis and risk assessment to network access control rules, each placed at a named enforcement point and traceable to the threat records behind it. It also presents a unified compliance framework that treats IEC 62443 and ISO/IEC 27001 as a fixed substrate and domain-specific regulation as an interchangeable overlay. The methodology is applied to an automotive original equipment manufacturer plant network, addressing multi-tier supplier authorization and legacy operational technology devices, and then to utility-scale battery energy storage, where North American Electric Reliability Corporation Critical Infrastructure Protection standards and ISO/IEC 27019 replace the automotive-specific standard while the substrate stays constant. In the automotive example, 37 threat entries and 24 cybersecurity goals yield 23 rules covering 11 of the 13 in-scope goals; the energy example yields six rules. Three derived rules have no equivalent in the cited standards. Both examples are desk-based: the methodology is applied to them, not tested on them. The result is a reusable pattern for translating risk assessment into enforcement across critical infrastructure sectors, with empirical validation left as future work.
Source:https://arxiv.org/abs/2611.09771PreviousLACART: Lookahead-Augmented Collision Avoidance and Reference Tracking Strategies for Safety-Critical RobotsNextAutonomous Whole-body Loco-manipulation Using a Snake Robot
Related posts
Trade onchain from an AI agent with Warden MCP
0dev.to
Teaching Your AI Assistant New Skills at Runtime — Without Rewriting It
0dev.to
Do AI coding agents care about SemVer?
0Microsoft DevBlogs

The Verge’s guide to Black Friday 2026
0The Verge

We’re giving away a bag filled with almost $900 worth of tech for Black Friday
0The Verge

Here are the shipping and return policies for all the big-name retailers
0The Verge