A local AI coding tool can reduce data exposure, but the word local does not answer the questions a...
Local-first doesn't guarantee security: it depends on explicit boundaries—readable files, permitted commands, credentials, external services. For CTOs, AI governance requires concrete boundary audit, separation of observation from execution, and evidence review before deploy—not assumptions about local.
A local AI coding tool can reduce data exposure, but the word local does not answer the questions a security review actually cares about.
The useful questions are narrower:
Which files can the agent read?
Which commands can it run?
Which network destinations can it reach?
Every time you send a query to a cloud AI API, you're sending data you don't control. For most use...
Running an agent on your own hardware fixes data sovereignty and nothing else. Prompt injection, silent provenance failures, and…
A practical comparison of cloud AI agents, local AI agents, and VPS-hosted setups, with tradeoffs across privacy, cost, latency,…
A while back I wrote about running local AI agents on your own code, and it became the most-read...
AI agents are quickly moving from simple chat interfaces to systems that can use tools, access data,...
A technical deep-dive into building private, on-premise AI agents using SGLang and Olares, including security audits, threat…
