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'Local' Solves Where Your Data Goes. It Doesn't Solve What Your Agent Does
TL;DR: Local deployment fixes data locality but not the behavioral risks of agents. It highlights data sovereignty as the real win, while leaving prompt injections, provenance issues, and privilege escalations unaddressed in on-prem setups.
Local models on hardware improve where your data goes, not what your agent does. They preserve data sovereignty for prompts, documents, and customer data, which helps with GDPR and compliance under evolving AI regulations. However, prompt injection success rates, silent provenance failures, and privilege escalation remain risks, just as in cloud deployments. Safe use is tied to task shape and input trust, not merely the hardware location. The key takeaway is: data location is the gain; behavior risk remains and must be mitigated separately.
Question for the room: What practical steps have you taken to mitigate prompt injection and provenance risks when moving agents on-prem?
— via dev.to
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