Tagged defense
3 pieces
- · 7 min read
Why Air-Gapped Changes Every Assumption in Your ML Pipeline
An air gap is usually treated as a deployment constraint, something to solve at the end with a USB drive and a procedure. It is not. It invalidates assumptions at every stage of the pipeline: how you get data, how you version artifacts, how you know what is running, how you evaluate, and how you find out anything went wrong.
- · 7 min read
AI at the Edge With No Network and No Retry
Every convenience in a cloud AI stack is a form of retry: call again, fall back to a bigger model, log it and fix it tomorrow. Remove the network and all of them go at once. What remains is a system that has to be right the first time, inside a power and latency envelope decided by hardware choices made months before anyone wrote inference code.
- · 7 min read
The Cloud Hides Your AI's Real Failures
Four properties of every model are always present and almost never visible: it extrapolates past its data, it discards most of your input, it is memory-bound on a shared bus, and its confidence off-distribution is undefined. A cloud environment hides all four, because a retry, a redeploy, a bigger box, and a human on a dashboard tomorrow each absorb one. Take the second chance away and they stop hiding at once.