How do AI and blockchain technologies contribute to defense mechanisms in IIoT systems according to the text?

AI and blockchain contribute complementary defense mechanisms in IIoT systems. AI, particularly deep learning models, detects and responds to anomalies faster than rule-based systems, and software-defined security architectures can adapt configurations dynamically based on real-time data analysis. Blockchain, integrated with AI, provides provenance frameworks that ensure transparency, traceability, and trust for verifying physical asset data across distributed IIoT platforms.

The text describes AI and blockchain as integral to modern industrial security infrastructures. AI-based defense includes deep learning models that outperform rule-based systems in the speed of anomaly detection and response, as discussed by Li et al. Rahman and Hossain envisioned software-defined security architectures for 6G-enabled IIoT, where security configurations change adaptively in response to real-time data analysis. Blockchain-based defense is exemplified by Umer et al.'s provenance framework, which is integrated with AI to guarantee transparency and traceability in cloud manufacturing, thereby enabling trust in distributed IIoT environments where asset data must be verified across multiple platforms. The text also notes that AI-based anomaly detection appears in layered, next-generation cybersecurity architectures, and that zero trust architecture principles such as continuous authentication, micro-segmentation, and role-based access support IIoT cybersecurity.