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Models come to your data — your data never leaves your infrastructure. You run a tracebloc secure environment on your own hardware. Peers submit models that are scanned and run in isolation against your data, on your machines. What leaves is results, model weights, dataset metadata and opt-out telemetry: after each training cycle your secure environment sends the weight updates to tracebloc, which combines them across secure environments.

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The trust boundary

How tracebloc keeps your data on your infrastructure — models come to the data, only results leave
Raw data stays inside your infrastructure. Your secure environment opens an outbound-only connection — nothing reaches in to pull your data out.

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How it works

1

Deploy your secure environment

One command sets up your secure environment on your own infrastructure — a laptop, an on-prem server, or a cloud cluster.
2

Ingest your datasets

Stage your training and test data locally. Metadata syncs to the platform so peers can see what’s available — the raw data never moves.
3

Create a use case

Define a use case from your datasets and set how submitted models are evaluated.
4

Invite peers

Whitelist peers by email. Only the people you invite can take part.
5

Peers submit and train models

Each model is scanned for vulnerabilities, then trains against your data in an isolated container, on your hardware.
6

Results and weight updates are shared

Training and evaluation results flow back to you over TLS. The model’s weight updates go to tracebloc after each training cycle, where they are combined. Whether data scientists can download trained weights is your choice: the Weight Files setting in the use case’s admin panel (on by default).

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What stays, what leaves

Control-plane logs (credentials redacted) and installer and CLI usage events (no paths, names or credentials) also leave, and you can opt out of both — see Security & data handling. Enforced by per-job container isolation, a NetworkPolicy that restricts training-pod egress, a vulnerability scan before any model runs, and TLS on all traffic.
The mental model: 1 machine = 1 secure environment = n datasets. One deployment per machine; as many datasets inside it as you like.

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Vocabulary

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What it touches

The installer adds Docker (if it’s missing), Homebrew on macOS (if it’s missing and Docker isn’t already running), a local Kubernetes cluster, the tools to run it (kubectl, k3d, helm), the tracebloc CLI (plus a PATH line in your shell profile, if needed) and ~/.tracebloc on your host. It switches kubectl’s current context to the new cluster, and on macOS it registers a launch agent that restarts the secure environment after a reboot. The full list is under Known footprint. Uninstalling is a single command, tracebloc delete, and your data is yours throughout.

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Get started

Quick Start

A running secure environment in about 10 minutes, with one command.

Deployment environments

Deploy on local / k3d, bare-metal, EKS, AKS, or OpenShift.
Want the guarantees in detail for your security team? See Security & data handling.