Security boundaries still matter for on-premise AI

01. What the sources establish

Joint international AI deployment guidance addresses the protection of models, infrastructure and operational security.

02. Our interpretation and proposed approach

Our checklist: document external APIs and telemetry, model provenance checks, document access rights, personal data in logs, backups and patching. Local execution alone does not remove leakage risks or institution-specific security requirements.

03. Questions for your own workflow

These are proposed review questions. They do not establish requirements for a specific institution.

Map external APIs and telemetry
Apply document permissions to retrieval results
Provide a way to report and correct wrong answers
Assign responsibility for model updates and patching

Source documents

Check each issuer and publication date. Media reporting is explicitly labeled.

Official source Deploying AI systems securely ↗New Zealand National Cyber Security Centre and international partners · 2024-04-16

Sources reviewed: 2026-11-05. For a correction, provide this page URL and the relevant source through SEJONG-AI.COM.

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Before choosing a local LLM,
define the conditions of the task.

Describe your documents, tasks, network constraints and expected concurrency. Discuss the deployment scope, evaluation and operating responsibilities.

You can start with the current constraints before selecting hardware or a model.

Prepare your inquiry

We are evaluating a local LLM in [network/on-premise environment]. Expected concurrency is [range], tasks are [tasks], available hardware is [hardware/undecided], and document permissions use [approach]. We would like to discuss scope, evaluation and operations.