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AI data leakage 7 min read

Why encryption does not protect your prompt

Encryption protects text from being read in transit. The recipient has to read it, or it cannot answer. That is not a flaw in the design; it is the design.

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Quick answer

Encryption protects your text from anyone watching the wire, and from anyone who walks off with a disk from the data centre. It does not protect your text from the service itself, because the service has to read it to answer. A language model cannot compute on ciphertext. The moment your message arrives it is decrypted, processed and usually stored. "Encrypted in transit and at rest" is a real control against outsiders. On the question of whether this data belonged in the tool at all, it says nothing.

01

Encryption in transit stops interception, not the recipient

02

Encryption at rest stops disk theft, not access by anyone holding the key

03

A model cannot compute on ciphertext, so your text is always decrypted

04

End-to-end encryption is structurally impossible for an assistant that replies

05

What you can still control is what the text contains, not how it travels

A security questionnaire comes back from a vendor with the encryption row ticked: in transit and at rest, both yes. Someone reads that as clearance for sensitive data. It is not, and the gap between those two statements is where many AI risk assessments quietly go wrong.

Encryption is a real control against a real threat. Just not this one.

What encryption does cover

Cloud services apply encryption in two standard places, and each does exactly one job.

In transit. Traffic between your browser and the provider's server travels over an encrypted connection. Anyone listening on the café network sees that you are talking to a service, not what you said. This is the padlock in the address bar, and it works.

At rest. Data sits encrypted on the data centre's disks. Anyone who removes a drive or obtains a backup tape gets nothing usable. This also works, against that scenario.

Both protect against an outsider. Neither protects against the party holding the key, which is the provider.

Why the recipient has to read it

This is the part that is rarely spelt out.

A language model computes on your text. It splits it into tokens, maps those to numbers and calculates the next token from them. That cannot be done on ciphertext: well-encrypted data is by design indistinguishable from noise, and there is nothing to compute on noise.

So the moment your message arrives, it is decrypted. It sits readable in server memory during processing, and afterwards it is usually stored so your history works. None of this is underhanded. It is the only way the service can function.

Which is also why an AI assistant cannot be end-to-end encrypted the way a messaging app is. Under end-to-end encryption only the recipient can decrypt and the intermediary cannot. With an assistant, the intermediary is the recipient. There is nobody else at the other end.

Our article on WhatsApp at work covers the same misunderstanding in a different setting: encryption protects the transport, not the recipient, the device or the question of whether the message should have been sent.

What happens to the decrypted text

Once decrypted, all the ordinary rules of the service apply:

  • The conversation is stored, with a retention period. See what a chat history actually is.
  • On suspicion of abuse, provider staff may review it.
  • On a consumer account it is used for training by default unless you opt out.
  • Under a preservation order it can be retained even after you delete it.

Encryption changes none of these four. They sit alongside it, and together they matter more to your assessment than the encryption row does.

Is there technology that fixes this?

Work is under way, but in all honesty it is not yet common practice.

Homomorphic encryption makes computing on encrypted data possible in principle, but remains orders of magnitude too slow for large language models. Some providers use secure enclaves (a walled-off region of the processor that even the operator cannot inspect) for parts of their pipeline, and that is a genuine improvement. It is a different thing from end-to-end encryption, and what falls inside the boundary varies by vendor.

The working rule for now: in an assessment, do not assume the provider cannot read your text unless they document precisely how that has been closed off.

What does help

Two things, both less tidy than a tick on a form.

The first is processing that keeps nothing. Text discarded right after the check never ends up decrypted on a disk at all. The questions to ask about that are set out in where your data goes.

The second is the text itself. Less sensitive data in the prompt means less sensitive data sitting decrypted on a server, however well that server is defended.

Not an exciting conclusion, but the only one that holds regardless of vendor, tier and year. Encryption governs how your text travels. What goes into it remains the decision of the person typing.

FAQ

Common questions

Are my ChatGPT conversations encrypted?

Yes, in the two standard ways: in transit between your browser and the server, and at rest on the provider's storage. That is industry standard and worth having. It does not mean the provider cannot read your text, because the provider holds the keys.

Why isn't AI end-to-end encrypted like Signal?

Because something at the far end has to compute on your text. Under end-to-end encryption only the recipient can decrypt and the service cannot. An assistant that cannot read your message cannot answer it. That is a property of the task, not a shortcoming of the vendor.

What does "encrypted at rest" actually mean?

That data sits encrypted on the data centre's disks. It protects against someone physically removing a drive or obtaining a backup. It does not protect against the provider's own systems, which hold the key and read the data as a matter of routine operation.

Is there AI that computes on encrypted data?

Homomorphic encryption and secure enclaves are areas of active development, and some providers use enclave-based processing for parts of their pipeline. For ordinary chat with a large model this is not how things work today. Do not assume it in an assessment unless the vendor documents exactly what is covered.

So what does help?

Two things. Processing that never leaves the device, because text that is not sent needs no protection in transit. And putting less sensitive data into the text in the first place. The second is unglamorous, but it is the only control that works regardless of vendor, tier or year.

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