Protecting the code that powers everything

In early 2023, Samsung engineers accidentally pasted proprietary semiconductor source code into ChatGPT -- three separate times in under a month. The code reached OpenAI's servers and could never be retracted. Samsung banned internal AI tool usage entirely, but the damage was done. That incident was not an anomaly. It was a preview of what happens when enterprise code meets consumer AI with zero guardrails.

The shadow AI problem is accelerating. 83% of developers now use AI coding tools daily, yet most enterprises have no mechanism to control what leaves the network. Traditional DLP solutions were designed for email attachments and Slack messages -- they cannot parse code semantics, and they certainly cannot protect against an engineer copying a function into a chat window.

Existing solutions rely on redaction: stripping out sensitive tokens before sending code to an LLM. The problem is that redaction destroys context. An AI model that receives code with half its identifiers blanked out produces degraded, often useless output. Pretense takes a fundamentally different approach: mutation. We replace every proprietary identifier with a deterministic synthetic equivalent that preserves the code's semantic structure. The AI sees fully coherent code and produces full-quality responses. When the response comes back, we reverse the mutation automatically. Nothing real ever leaves the machine.