By Lucas Desfossez · Rawrr project operator
To let a creative team use AI without sending confidential inputs to a remote inference provider, start with an approved local workflow on managed workstations. Then verify network activity, extensions, storage and sharing. Local processing reduces one exposure path; it is not a guarantee against every leak.
Map the workflow before approving it
List where briefs, reference images, prompts, outputs and any training data are stored and processed. Include model downloads, inference nodes, plugins, cloud-synced folders, crash reports and backups. “Not used for training” and “never leaves the device” are different claims; ask which applies to each component.
A practical pilot for a studio director
- Choose a low-risk brief. Use synthetic or already-public assets and define a measurable creative task.
- Assign a managed workstation. Keep operating systems and drivers maintained, control access and use your studio’s approved storage.
- Approve models and extensions. Review sources and commercial-use permissions. Restrict unreviewed plugins and remote API nodes.
- Observe data movement. Check network traffic during the actual workflow. If offline operation is a requirement, test it after downloads and document what stops working.
- Review outputs and costs. Record quality, processing time, GPU memory, failure cases and the artist’s editing time.
- Approve a sharing and deletion process. Decide who can export, where files may go and when source assets and adaptations must be removed.
Treat a client-specific LoRA as a sensitive asset
A LoRA trained on confidential references can itself be sensitive. Keep the dataset and weights inside the approved storage boundary, restrict access and review retention. Do not assume that deleting the reference images also deletes the adaptation or copies of it.
Questions to ask any AI supplier
- Which operations execute locally, and which call external services?
- What telemetry, logs and crash reports are sent?
- Can extensions or updates add new network destinations?
- Who can access datasets, weights and generated files?
- Which controls are implemented and independently verified, rather than planned?
Is Rawrr ready for an enterprise rollout?
Rawrr is in development, with a closed beta planned for mid-November 2026. Its local-first direction may suit teams evaluating workstation-based creative AI. Enterprise administration, SSO, audit logs, certifications, team deployment and complete offline coverage are not established claims on this site. Evaluate the actual beta before approving confidential production work.
When applying, describe the workflow and hardware at a high level. Do not send client files or confidential briefs through the website form; application details are delivered through Cloudflare to the operator’s email inbox.