Large Language Models are rapidly finding their way into everyday software. They write our emails, summarize documents, assist with coding, support customer service, analyze text, and automate workflows. As more applications silently integrate AI into their core features, it becomes increasingly difficult to understand what information is being sent to these systems and how much data leaves the device during normal use. What once felt like a simple interaction between a user and an application has grown into a large and opaque black box.

With this expansion comes a growing surface for errors and attacks. Prompt injections, for example, have become far more subtle and much harder to detect with the human eye. Hidden inside translations, formatting, encoded text, system prompts, or multi-step conversations, they can manipulate model behavior, leak internal information, or trigger harmful actions. Organizations cannot rely on users to spot these attacks or on cloud services to "just handle it". Real protection must happen before data ever leaves the device, keeping integrity on the device.

At the same time, the risk of unintentionally leaking sensitive information is increasing. LLMs can process nearly anything - documents, emails, screenshots, internal notes, customer data, or source code. But should all of this be sent to an external AI model? In most cases the answer is: no. Yet today, most systems transmit data immediately to cloud-based AI services without any local review, filtering, or enforcement mechanisms. This creates a dangerous gap: once data is sent, it cannot be pulled back, audited, or easily controlled.

Patronus Protect was created to address these two problems directly. Our mission is to build an on-device AI firewall that analyzes, filters, and protects AI interactions at the point where they originate: the user’s own device. We aim to detect prompt-based attacks before they reach the model, to prevent internal context from being manipulated, and to block or sanitize harmful inputs. At the same time, we ensure that sensitive data is identified and protected locally, so that private or regulated information is never transmitted unintentionally.

We are a young, fast-moving team actively building our MVP, combining modern on-device inference, system-wide visibility, prompt-injection detection, PII protection, and practical safeguards for real users. Over the coming weeks and months, we will share updates, early demos, research insights, and more about how we are making AI interactions both safer and more transparent.

Stay tuned - the era of on-device AI security is just beginning and Patronus Protect will be a part of it.