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“Can we use AI here without sending sensitive data everywhere?”

Sensitive workflows do not necessarily rule out AI, but they change the architecture. Data minimization, execution location, provider boundaries, retention, model access and audit evidence must be explicit rather than hidden behind a generic SaaS integration.

private AIon-premiseEU hostingLLM gateway
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DEMAND LANGUAGE / REAL-WORLD PROBLEM

Does this sound familiar?

“Can we use AI without sending sensitive data everywhere?”
“What really needs to run on-premise?”

WHAT CAUSES THIS?

Why it breaks in production

Data leaves the intended trust boundary through prompts, logs or telemetry.

  • Provider retention and subprocessors are not mapped to data classes.
  • Local models are selected without measuring quality or operational cost.
  • Private execution is assumed to solve authorization automatically.

architecture_for PRIVATE LOCAL AI WORKFLOWS

engineering

We map data classes and required capabilities, then choose the narrowest execution path that meets quality, latency, security and cost requirements. Hybrid routing is often more practical than one universal deployment model.

security

authority

Encryption, network boundaries, workload identity, retention controls and provider contracts are treated together with application authorization.

performance

critical

Private execution is benchmarked for quality, throughput, latency, hardware utilization and total cost rather than selected on ideology.

technologies

vendor

private AI · on-premise · EU hosting · LLM gateway

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  • Send full documents when a narrow extracted context is sufficient.
  • Assume “on-prem” means secure by default.
  • Use one model route for every data class.
  • Ignore logs, traces and backup copies.

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  1. Data-flow and retention audit.
  2. No-egress tests for restricted classes.
  3. Quality benchmark across approved model routes.
  4. Latency/cost measurement under representative workload.

CTO / CIO FAQ

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Does sensitive data require on-premise AI?

Not always. Requirements may be met by controlled EU-hosted or private-cloud execution depending on policy and data class.

Can we route different data to different models?

Yes. A policy-aware gateway can select approved execution paths by data class and task.

Is a local model automatically safer?

No. Identity, authorization, logging, patching and data handling still need engineering controls.

connected

related

Legal document intelligence needs evidence, isolation and reproducibility at collection scale. →Treat AI agents as machine actors with explicit identity and capabilities. →Enterprise knowledge retrieval is a source, permission and evidence problem before it is an AI problem. →
research →technology →products →

assessment

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Discuss a problem with an engineerRequest a technical assessment