LiquidAI's ShieldFlow Scrubs Sensitive Data Before It Reaches the Cloud

Every time a user pastes a document into an AI assistant, there is a moment where sensitive data crosses a boundary it probably shouldn't. Names, social security numbers, financial records, health information , all of it travels to a remote server before the model ever sees the prompt. Liquid ShieldFlow is Liquid AI's answer to that problem: a privacy layer that runs entirely on the user's device and scrubs sensitive data before any prompt leaves the machine.

A guard at the gate, not the cloud

The core idea is straightforward. ShieldFlow is an on-device privacy layer that redacts sensitive data and prevents prompt injection before any prompt leaves the device. It sits between the user's input and whatever AI system they're calling , whether that's a local model or a cloud API , and sanitizes the text in real time.

What makes this technically interesting is what's powering it. ShieldFlow runs on a device-native Liquid Foundation Model (LFM) , Liquid AI's proprietary model architecture , rather than a rules-based regex engine or a cloud-side classifier. That means it can understand context, not just pattern-match on known PII formats. The tradeoff with most on-device AI is hardware requirements, but Liquid is explicit that ShieldFlow needs no GPU and is light on memory, making it viable on ordinary laptops and PCs.

The architecture behind it

Liquid Foundation Models leverage Liquid Neural Networks, a proprietary architecture rooted in dynamical systems and signal processing, to deliver frontier-grade intelligence at a fraction of the compute, on any hardware, on or off the cloud. The LFM2 generation that underpins ShieldFlow is a hybrid model combining short convolutions with grouped query attention , a design that trades some of the memory overhead of pure transformer architectures for significantly better CPU performance.