Securing AI Coding Assistants

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Empower developers with AI — without compromising your codebase 

AI-powered coding assistants are rapidly becoming essential tools in every developer’s workflow. From GitHub Copilot and ChatGPT to in-house IDE integrations, they’re speeding up code generation, fixing bugs, and suggesting improvements at scale. But there’s a hidden cost: security risks that live inside your source code, exposed through invisible data leaks, LLM misuse, and vulnerable suggestions. 

At Mirror Security, we secure the AI-assisted development lifecycle—so your engineers can move fast without opening the door to breaches.

Unlock new horizons by partnering
with Mirror Security

Whether you’re a cybersecurity distributor safeguarding your clients’ AI or seeking a strategic tech partnership to integrate robust AI security, partnering with us will enable you to contribute to a future of safe and trustworthy AI.

AI Coding Plugins Are Powerful — and Risky

AI Coding Plugins Are Powerful — and Risky

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. 

AI Assistants and Codebases: The Trust Gap

AI Assistants and Codebases: The Trust Gap

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

What code are AI assistants actually seeing?

What code are AI assistants actually seeing?

What code are AI assistants actually seeing?

Where is that data going?

Where is that data going?

Where is that data going?

Are AI-generated suggestions reusing internal logic inappropriately?

Are AI-generated suggestions reusing internal logic inappropriately?

Are AI-generated suggestions reusing internal logic inappropriately?

With no access control, no audit trail, and no encryption during generation, your codebase becomes vulnerable—even when developers mean well. 

Real Example: ChatGPT Code Generation Risks

Real Example: ChatGPT Code Generation Risks

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: 

It's processed by external models

It's processed by external models

It's processed by external models

It may be stored in chat logs or vector databases

It may be stored in chat logs or vector databases

It may be stored in chat logs or vector databases

It can accidentally reappear in responses to other users

It can accidentally reappear in responses to other users

It can accidentally reappear in responses to other users

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. 

How Vector Databases Increase the Risk

How Vector Databases Increase the Risk

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.

Mirror Security: Our Solution to Secure AI-Assisted Development

Mirror Security: Our Solution to Secure AI-Assisted Development

We designed Mirror Security to protect AI-enhanced development from the inside out.

Fully Homomorphic Encryption (FHE) for Vector Memory

Fully Homomorphic Encryption (FHE) for Vector Memory

We encrypt code embeddings stored in vector DBs using FHE, meaning the data stays encrypted at all times, even while being searched or processed. 

  • Perform high-speed, encrypted similarity search. 

  • Store and retrieve vector embeddings with full privacy. 

  • Protect LLM inputs and outputs from reverse engineering. 

  • Perform high-speed, encrypted similarity search. 

  • Store and retrieve vector embeddings with full privacy. 

  • Protect LLM inputs and outputs from reverse engineering. 

Secure AI Plugin Enforcement

Secure AI Plugin Enforcement

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.

Real-Time Prompt & Response Filtering

Real-Time Prompt & Response Filtering

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.

Secure Vector Search for Code

Secure Vector Search for Code

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.

SDKs for Safe AI Assistant Development

SDKs for Safe AI Assistant Development

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: 

Data at Rest: Encrypted, compliant, and access-controlled

Data at Rest: Encrypted, compliant, and access-controlled

Data at Rest: Encrypted, compliant, and access-controlled

Data in Use: Secured by FHE during vector and LLM processing

Data in Use: Secured by FHE during vector and LLM processing

Data in Use: Secured by FHE during vector and LLM processing

Data in Motion: Fully protected with enterprise-grade TLS and logging

Data in Motion: Fully protected with enterprise-grade TLS and logging

Data in Motion: Fully protected with enterprise-grade TLS and logging

Mirror Security

© All rights reserved

Mirror Security

© All rights reserved

Mirror Security

© All rights reserved