Every major cloud AI tool has the same privacy problem, and it is structural rather than incidental. The business model of cloud AI depends on data flowing through centralised infrastructure — it is not a bug that can be patched, it is the architecture. Understanding this clearly helps you make better decisions about what to use these tools for and what to keep away from them.
The Structural Problem
Cloud AI tools process your data on servers they control. Even with strong privacy policies and opt-out settings, the data physically travels to and exists on third-party infrastructure during processing. This creates three categories of risk that cannot be engineered away: breach exposure (data in transit or at rest can be intercepted or leaked), policy risk (privacy policies change, companies get acquired, regulatory environments shift), and inference risk (even without storing conversations, metadata and usage patterns reveal information about you).
The response "but they have strong privacy protections" is a policy answer to a structural question. The question is not whether they are trying to protect your data. The question is whether you should be accepting the risk at all for sensitive use cases.
The Only Genuine Solution
Local processing eliminates these risks categorically. When the model runs on your machine, there is no transit, no third-party server, no policy to rely on. The risk profile is fundamentally different — you are now responsible for your own machine's security, which is a risk you already manage for all your other sensitive data.
Skales with Ollama provides this architecture. Every conversation, every file, every automation log stays in ~/.skales-data on your computer. No Skales servers exist to breach or subpoena. Read our full privacy breakdown or download free and verify the architecture yourself.