As scaled-down circuits with limited functions redefine computing for AI, their flexibility requires a new approach to securing critical infrastructure.

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Dark Reading

Chiplets — modular semiconductor designs that stitch together smaller silicon components from multiple vendors — are reshaping AI data centers and autonomous vehicles, but their distributed manufacturing model introduces serious cybersecurity risks. Unlike monolithic chips with a single vendor, chiplets involve global supply chains where any component could harbor hardware Trojans, backdoors, or vulnerabilities that propagate across entire product families. Experts from Binarly, Synopsys, and Athos Silicon highlight the need for per-chiplet identity, authentication, secure-boot mechanisms, and rigorous supply chain audits. The automotive sector, governed by ISO 26262 and ISO 21434, is leading the charge, with companies like Athos Silicon bringing design in-house and physically auditing suppliers. Industry standards bodies like the UCIe Consortium are working on frameworks, but the threat landscape is evolving faster than standardization efforts.

Emerging Chiplet Designs Spark Fresh Cybersecurity Challenges