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Ross Peili
Ross Peili

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Stop choosing between LLM intelligence and PII compliance

#ai #security #privacy #compliance

Choosing non-sovereign LLM inference should NOT equal the shortening of PII compliance in 2026.

Considering the latest leaks, hacks, and severe security compromises of even top-tier AI behemoths, the obvious elephant in the room is even more apparent, reminding us that data leakage is the number one barrier to pragmatic enterprise AI adoption, that is beyond fancy chatbots that farm media headlines as a KPI.

Sending raw prompts to the cloud is not only a risk of private employee data leaving your premises, but a risk that subjects the profitability and even the vitality of an entire business model.

At the same time, building basic custom filters on 70B parameter models is an unjustifiable cost, to say the least, if not straight-up absurd.

For that, we’re releasing F1 Mask, our first open weights model in the new ARPA Micro series. We're looking at a tiny 270M parameter middleware agent designed to act as a local privacy firewall. Built on the pop Function Gemma 3 base, it identifies and tokenizes Personally Identifiable Information (PII) in under 50ms before it ever hits a cloud API.

On top of that, "model template," if you'd like, we are releasing a set of scripts that will help you generate high-entropy synthetic datasets for your operational needs, train the model locally in less than 15 minutes, and evaluate its performance based on your expectations.

You can find the source code, including the tutorial on how to tailor the model to your PII needs, on GitHub: github.com/arpahls/micro-f1-mask.

If you're looking to download the weights, HuggingFace offers an Apache 2.0 version of the trained model: huggingface.co/arpacorp/micro-f1-mask.

If you wanna test the base engine before you commit, call it from Ollama via:

ollama run arpacorp/micro-f1-mask
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Why it matters for Critical Infra:

  • 💡 Zero-Latency: Sub-50ms inference on standard hardware (RTX 2070).
  • 💡 Privacy by Architecture: Sensitive data stays in your Redis vault; the cloud only sees tokens like [INDIVIDUAL_X], [EMAIL_Y], [IBAN_Z].
  • 💡 Highly Customizable: Ships with a synthetic generator to retrain the model on your specific industry edge cases.

Low effort, high impact, and zero PII compromise.

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