Secure GenAI with Confidential Computing

We wanted to bring AI into our engineering workflows and business operations—from knowledge access to process improvements. But given the sensitivity of our data and the environments we operate in, we knew it couldn’t be just any AI solution. 

Our security bar is high, and for good reasons. 

To confidently adopt GenAI, we needed solutions that aligned with the five foundational pillars of GenAI security: Security, Privacy, Compliance, Governance, and Data Sovereignty. 

What we found instead were tools that felt half-baked, lacking in runtime protection, observability, and control. None of them could meet all five pillars end-to-end. 

There was no product that passed the acid test. Security had to be part of the foundation and not an afterthought—and we refuse to compromise trust just to chase hype. 

That’s when we realized: to do GenAI right, we would have to build it ourselves—with Confidential Computing at its core. 

The Real Risk: When AI Touches Sensitive Data 

GenAI systems are powerful, but they often require access to: 

• Proprietary documents 
• Customer records 
• Regulated data sources (PHI, PII, financial data) 

In traditional systems, even if data is encrypted at rest or in transit, it must be decrypted during processing—in memory—making it vulnerable to: 

• Insider access 
• Cloud infrastructure compromise 
• Prompt injection or model tampering 

That exposure is a deal-breaker for many real-world use cases, and it was for us.  

Why Confidential Computing Matters for GenAI 

Traditional encryption methods protect data at rest and in transit. But once data needs to be processed, it’s decrypted in memory—creating a vulnerable attack surface. 

Confidential Computing solves this by leveraging Trusted Execution Environments (TEEs)—secure, hardware-based enclaves that isolate data and computation from the rest of the system. 

Sensitive data and models are processed securely, without ever exposing the raw data—even to the host OS or cloud provider. 

Securing GenAI RAG Pipelines with Confidential Computing 

Retrieval-Augmented Generation (RAG) pipelines unlock powerful capabilities by combining large language models with real-time, domain-specific data.  

However, this also introduces new security, privacy, and compliance risks, especially when dealing with proprietary, sensitive, or regulated information. 

Our platform embeds Confidential Computing directly into the RAG pipeline—ensuring every layer of the GenAI stack is protected, from document ingestion and vectorization to inference.  

By securing both retrieval and generation, our Confidential AI platform enables safe adoption of RAG pipelines in industries like finance, defense, and enterprise SaaS—without compromising security and trust.  

Secure GenAI starts with Fortanix 

We believe Confidential Computing is the foundation for trusting Gen AI.  

Our mission is to empower to innovate with GenAI—without compromising security, privacy, or compliance.  

Today, we’re proud to launch the Public Preview of ArmetAI: a secure foundation for Retrieval-Augmented Generation (RAG) pipelines that don’t force you to choose between innovation and trust. 

Join the Public Preview → Armet AI : Secure, Turnkey GenAI with Confidential Computing | Fortanix