LOCKThe privacy question everyone is asking
Every prompt you send to a cloud model leaves your machine. Policies vary. For consumer toys that may be fine. For client data, unpublished product plans, medical context, or regulated work, “trust us” is not a control plane.
What private AI actually means
Private AI means prompts, documents, and generated results default to staying under your authority — ideally on hardware you own or a tightly controlled on-prem environment. It is not a sticker on a SaaS login. Architecture decides privacy.
Cloud AI strengths
Fast start, managed uptime, frequent model upgrades, and low friction for public or non-sensitive tasks. Ideal for experimentation and non-confidential drafting when contracts allow.
Local private AI strengths
Data residency by construction, offline options, no automatic multi-tenant model hop, and capacity that scales with your hardware budget instead of per-seat surprise bills.
Hybrid honesty
Many organisations will mix paths: local for sensitive work, cloud for public research. The critical design is that sensitive paths must not fall back to the cloud by accident.
S◉LOCK stance
S◉LOCK is designed local-first with no automatic cloud model fallback. Founding Preview invites buyers who need real privacy architecture — not another rented chat seat.
Next step: Explore the Helmsman model catalog, check hardware floors, or request Founding Preview access.
FAQ
- Is private AI the same as encrypted chat?
- Encryption in transit helps, but if the model runs in a vendor’s multi-tenant cloud, the vendor’s stack still processes content. Local inference removes that hop.
- Does GDPR require local AI?
- Not always — but EU data-protection culture favours minimisation and control. Local-first design aligns with European expectations when you process sensitive personal data.