Retrieval-Augmented Generation (RAG) is a technology designed to enhance the effectiveness of large language models (LLMs) by utilizing tailored data. RAG leverages specific data or documents as context for the LLM to improve effectiveness, support current information, or provide domain-specific expertise. In simple words, it allows large language models (LLMs) to answer questions about data they weren't trained on.

Retrieval-Augmented Generation (RAGs) are used to enhance Large language Models' (LLMs) output. By default, Large language Models (LLMs) are trained on vast and diverse public data, and they do not necessarily have access to resent information. This leads to potential inaccuracies, or hallucinations, on unfamiliar data queries that deem the LLM useless.

For organizations that require LLMs to offer precise responses tailored to their domain, the model needs to use insights from their data for specific answers. Retrieval-Augmented Generation (RAGs) have become the industry standard that allows non-public data to be leveraged in LLM workflows, so users can benefit from accurate and relevant responses.

Retrieval-Augmented Generation (RAG) enhances the response quality of Large Language Models (LLMs) by using current and contextual external data sources. This approach effectively minimizes inaccuracies in the generated answers and delivers tailored, domain-specific information, allowing organizations to get real advantage of their AI deployments.

1. Data Breach and Exposure 

Retrieval-Augmented Generation (RAG) systems rely on vast amounts of data for both retrieval and generation, and this data is stored in vector databases. The security offered by vector databases is immature, so malicious actors could exploit weaknesses to gain access to sensitive and PII data. If not properly secured, this data can be vulnerable to breaches and unauthorized access, leading to data exposure and violation of numerous data privacy laws and regulations, such as GDPR, HIPPA, CCPA, and more.  

2. Model Manipulation and Poisoning 

AI models, including those used in Retrieval-Augmented Generation (RAG) systems, are susceptible to manipulation and poisoning attacks. Bad actors can feed the system with corrupt or misleading data, causing it to generate harmful or misleading responses. This not only undermines the reliability of the AI but also poses significant security risks. 

3. Inaccurate or Misleading Information 

Even with the combination of retrieval and generative models, there is still a risk of producing inaccurate or misleading information. If a Retrieval-Augmented Generation (RAG) system is fed with outdated or incorrect data, the generative model may amplify these errors, leading to the spread of misinformation. 

The data security recommendations and best practices mentioned for Large Language Models (LLMs) are equally applicable to Retrieval-Augmented Generation (RAG) models. 

OWASP Top 10 for Large Language Model Applications

https://owasp.org/www-project-top-10-for-large-language-model-applications/ 

NIST AI Risk Management Framework (AI RMF 1.0) Explained  

https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf 

A GPU TEE isolates workloads in hardware-protected memory so that data and code stay encrypted even during computation. It ensures that no outside process, including system administrators, can see what’s running inside. 

NVIDIA’s Hopper and Blackwell architectures include built-in confidential computing capabilities. They pair GPU-level encryption with remote attestation to verify that workloads are running in trusted mode. 

The overhead is minimal thanks to hardware-accelerated encryption and optimized memory access. The small trade-off delivers significant gains in data assurance. 

This depends on the workload type, but performance loss is often under 5%. For most AI inference and training tasks, the impact is minimal compared to the added protection of keeping data encrypted in use. 

CPU-GPU transfers can slow down performance because moving data between memory pools adds latency. Confidential computing mitigates this by encrypting and verifying data while in transit, keeping security intact without creating excessive overhead. 

Yes, in confidential computing mode. NVIDIA GPUs encrypt the data stored in GPU memory, so sensitive information is protected even when it’s being processed. 

Verification is done through attestation, which generates cryptographic proof that the GPU is operating in a secure and verified state. Systems like Fortanix Confidential Computing Manager automate this validation before data is ever decrypted. 

GPU attestation confirms that both hardware and software components are running trusted, untampered firmware and drivers. It forms part of that larger “chain of trust” that secures workloads from the chip level upward. 

Common use cases include secure AI training, privacy-preserving analytics, healthcare diagnostics, and regulated data processing. It enables organizations to run sensitive workloads on GPUs without exposing the raw data. 

Absolutely. Confidential computing allows models to train encrypted data inside trusted enclaves, protecting both the training data and the model weights from unauthorized access. 

Scaling effectively requires centralized key management, coordinated attestation across regions and consistent policy enforcement, so workloads can securely move between on-prem and cloud GPU environments.

Encrypt model weights at rest, in transit and during use within a trusted execution environment. Key release should be gated by attestation, so only verified hardware and workloads can access the model. 

Attackers are increasingly targeting side-channel leaks, firmware vulnerabilities and supply-chain tampering. This is why continuous attestation, hardware updates, and runtime monitoring are critical in defending against these threats.