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AI Security Is Now a Core Part of Linux Infrastructure

Linux Journal
2 months 1 week ago
by Malana VanTyler

AI security means hardening everything around the model, not just the model itself.

AI workloads have moved out of the research sandbox and into production, which means they now carry the same operational weight as any other critical service. If you want to understand what is AI security in practical terms, start here. It's not a 

separate discipline bolted onto your existing stack. It's an extension of it. The systems around a model matter as much as the model itself, and for teams running Linux and open-source infrastructure, that means applying familiar discipline to some new risks.

AI Security Is Becoming Part of the Linux Infrastructure

Treat AI workloads like production infrastructure, not side projects. A model doesn't run in isolation. It depends on an operating system, a container runtime, storage, APIs, identity systems, and a pile of dependencies. If any of those layers are weak, prompt filtering or output moderation won’t be enough.

Security teams that only look at the model may miss much of the actual attack surface. The work starts with the same questions you'd ask about any service. What's exposed? Who can reach it? What happens if it's compromised? 

The AI Attack Surface Extends Beyond the Model 

Model-serving APIs, embeddings, retrieval systems, vector databases, plugins, and CI/CD pipelines all now sit inside the perimeter. A vector database that holds proprietary documents is a data store like any other and needs the same access controls. An inference endpoint that accepts arbitrary text input is a public-facing service, and it needs rate limits and authentication like any other. Plugins and tool integrations expand what a model can touch, which also expands what an attacker can touch. It’s the same threat modeling you’d apply to a web application. 

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Malana VanTyler

Jensen Huang and Lisa Su Snubbed By TIME's 2026 List of Top 100 AI Leaders

Slashdot
2 months 1 week ago
TIME's 2026 list of the 100 most influential people in AI omits Nvidia CEO Jensen Huang and AMD CEO Lisa Su, despite both having appeared on previous editions and playing central roles in the hardware powering the AI boom. Nvidia's only representative this year is sustainability chief Josh Parker, while the list includes figures such as Ben Affleck and Paris Hilton alongside major tech executives and policymakers. Tom's Hardware reports: Broadcom CEO Hock Tan, Micron CEO Sanjay Mehrotra, SMIC co-CEO Liang Mong Song, and Huawei HiSilicon president He Tingbo all appear on the 2026 list, joined by Cerebras' Andrew Feldman, CoreWeave's Michael Intrator, and Nscale's Josh Payne. Together, the roster includes Nvidia's HBM supplier, two of its biggest GPU cloud customers, its custom-accelerator rivals, and China, which is trying -- and so far struggling -- to replace Nvidia altogether. Parker's profile, which appears under the Thinkers category, focuses on data center energy and water consumption, alongside Nvidia's tripling of Scope 3 emissions in two years, and the Rubin platform's shift to 100% liquid cooling. "Nvidia is at the center of the AI revolution," Parker told TIME. TIME editor-in-chief Sam Jacobs wrote in the list's accompanying article that selection was led by editor Ayesha Javed, and that the 100 honorees represent the year's "key storylines and who, in our opinion, are having the most influence in driving these developments." Now in its fourth year, this edition of the TIME100 AI appears to have focused especially on featuring the new additions to TIME's community of AI leaders, with Mark Zuckerberg, Demis Hassabis, Sundar Pichai, and Satya Nadella representing four other honorees, arguably deserving of a space on this year's list, churned out. Su's strong TIME back catalog makes her omission all the stranger still. The magazine named her its 2024 CEO of the Year, put her on the 2024 AI list and the main TIME100 in 2025, and included her among December's Architects of AI.

Read more of this story at Slashdot.

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