Tools like Copilot, ChatGPT, and Amazon CodeWhisperer act like a second brain for developers. They can autocomplete entire functions, translate natural language into code, and even pull up helpful snippets from docs or repositories.
But most of these tools are connected to third-party models and APIs. That means your internal codebase, credentials, or business logic might be silently shared or stored outside your perimeter.
Today’s AI tools don’t just live in the browser—they’re sitting inside your developers’ IDEs, terminals, and CI pipelines. They're plugged into your Git repos, docs, and even private dev environments.
It’s a powerful setup—but with zero visibility into what’s being sent out or generated.
This creates a trust gap:
With no access control, no audit trail, and no encryption during generation, your codebase becomes vulnerable—even when developers mean well.
It’s become common for developers to paste internal code into ChatGPT for debugging or improvement. Seems harmless, right?
The problem is, once that code is submitted:
Even worse, AI models can suggest snippets that unintentionally contain other organizations' code, exposing everyone to IP infringement risks. If your dev team isn't aware, you might unknowingly ship someone else's proprietary code.
This risk is invisible, untracked, and impossible to undo once exposed.
Many AI coding tools now rely on vector databases to improve suggestion accuracy. These databases store embedded representations of past interactions, code snippets, or documentation—enabling smarter autocomplete and memory.
But when these embeddings contain sensitive code, they become silent leak points. Worse, these vectors can be reverse-engineered to reconstruct the original source, violating IP, compliance, or customer data agreements.
We designed Mirror Security to protect AI-enhanced development from the inside out.
Gain visibility and control over what AI tools can access inside your dev environments. Define policies to block outbound traffic from sensitive files, repositories, or even certain environments.
Use Mirror’s policy engine to scan both AI prompts and outputs. Prevent sensitive data—like secrets, tokens, or PII—from being exposed in any direction.
Enable fast, secure search and retrieval of internal code snippets using encrypted vector indexing—perfect for safe RAG (Retrieval-Augmented Generation) experiences.
Developer-Level Audit Trails
Track every AI interaction: what was queried, what was returned, and which developer made the request. Build accountability into the dev workflow without slowing anyone down.
Building your own AI assistant? Use our SDKs to train or fine-tune LLMs inside your secure perimeter. Control exactly what data they access and prevent contamination of shared models.
RRR Platform: End-to-End Protection for AI Coding
Mirror’s RRR Platform (Recon Remediate Reinforce) locks down your AI code lifecycle: