Unlinkable inference to protect user identity
In a post published on February 26, 2026, OpenAnonymity describes a layer designed to prevent AI providers from linking inference calls to individual users.
Source: OpenAnonymity propõe inference não vinculável para IA (openanonymity.ai). Text prepared with AI from this source.
What happened and what to do
In a post dated February 26, 2026, OpenAnonymity describes a layer intended to prevent AI providers from linking inference calls to individual users. The post says the approach was integrated into open-source infrastructure and a chat app; engineers evaluating hosted models can examine this way of separating identity from requests.
A company can assess how its applications send prompts and metadata to AI services, map identification risks, and implement controls to reduce identity exposure. The work could include a request-routing architecture, privacy monitoring, and tests, without assuming that separation eliminates every risk.
How the consultancy can help
Wendelmaques can diagnose data flows and identification risks, define a scoped set of controls, and implement and operate the solution on the client’s infrastructure, including routing, monitoring, and tests.
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