Abstract

Industrial Internet of Things (IIoT) rapidly reconfigures business models by enabling machines to make more autonomous decisions. Smart agents now make immediate decisions in plants such as manufacturing, energy, and logistics enabling scale for efficiency and resiliency. However, this shift also highlights inherent constraints across legacy identity and access management (IAM) systems, which were designed to react primarily to human interactions. Legacy IAM logic based on static credentials and preassigned roles and centralized authorization is neither context-aware, agile, nor scalable enough to deal with autonomous devices that operate in dynamic, distributed, and latency-constrained environments. This work introduces a novel Intent-Aware IAM framework, tailored for autonomous IIoT systems. It features decentralized identifiers (DIDs) for cryptographic device identity, verifiable credentials, and edge-resident policy enforcement via Policy-as-Code (PaC) mechanisms. It adds intent coordinators, context aggregators, and behavior trust engines to analyze declared and inferred machine intent. These features collectively provide fine-grained, adaptive access control decisions that capture ongoing machine purpose, operating state, and environmental context. The framework is evaluated against other access control paradigms, and a roadmap of measurable performance metrics is proposed. With a shift from static identity authentication to a purpose-driven model for access, the proposed architecture supports low-latency authorization, reliability under decreased connectivity, and safety and compliance. Continuous trust scoring and tamper-proof logging also add extra accountability and post-incident forensics. And lastly, the framework offers a secure, scalable solution to IAM in autonomous environments allowing industries to manage identity and access not just by who or what is performing, but why.

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