Schneier on Security

Tris Simondsen • July 16, 2026 1:17 PM

Solove is entirely correct that the “user control” paradigm is dead. But shifting to “corporate accountability” and “algorithmic liability” introduces a massive verification problem: How do you formally audit the inferences of an autonomous AI?

In classical software, data minimization is a database problem; you simply restrict the fields you collect. In AI, this approach fails. A model can infer latent, highly sensitive attributes from seemingly innocuous observations. You cannot regulate this effectively at the point of collection; you must regulate the epistemic boundary of the agent itself.

If we are to enforce “rigorous data minimization” in an AI era, we need a mathematical architecture for zero-trust inference – the Principle of Epistemic Sovereignty (PES):

https://trissimondsen.wordpress.com/2026/07/16/the-principle-of-epistemic-sovereignty-formalizing-the-zero-trust-boundary-in-ai/

PES treats data minimization not as a policy preference, but as a strict measure-theoretic constraint. It requires that an agent’s posterior inferences depend only on a strictly authorized, F-measurable information interface. If an algorithm’s output relies on “outside-F” dependencies—smuggling in latent, unobservable completions to make its inferences, it violates the Non-Circularity Principle (NCP).

Under this framework, a structural breach of the epistemic boundary isn’t a vague “negligent design” issue; it is a mathematically provable violation of the agent’s authorized interface.

If we want Solove’s vision of algorithmic liability to survive contact with frontier AI, we must move past legal definitions of privacy and establish verifiable, F-measurable boundaries on what a system is mathematically licensed to “know.”