RAG on company data and complete security? AWS shows that it’s possible and simple!

Retrieval-augmented generation (RAG) is a modern approach that combines the capabilities of large language models (LLMs) with access to external knowledge sources, such as customer databases. Instead of relying solely on the model’s built-in knowledge, RAG dynamically searches and uses up-to-date information from a database or company documents. How does AWS approach this technique and what security features does it provide?

What is RAG and why is it so important?

RAG (Retrieval-Augmented Generation) is an artificial intelligence approach that combines the general capabilities of a Large Language Model (LLM), such as ChatGPT, with knowledge individually tailored to the needs of a company. The key feature of RAG is that it is not limited only to the information on which the model was originally trained (i.e., the so-called “world” knowledge base up to 2023 or 2024). Instead, it enables the model to be “fed” with your own data—company document databases, files, emails, or internal databases.

How does it look in practice? The process is quite intuitive:

• We use a ready-made (off-the-shelf) language model – we don’t need to train it from scratch.
• We add a retrieval layer to such a model: the model can “search” defined company knowledge repositories (e.g., PDF documents, databases, Word files, CRM systems).
• When someone asks a question, the system first searches for the most relevant fragments from the company knowledge base and then provides them to the LLM as context for generating a response.

Thanks to this, generated responses are not only well-formulated linguistically but also current and precisely matched to the company’s reality, as they are based on the latest internal data fed into the system.

The importance of RAG stems primarily from its ability to solve key problems of modern AI systems, such as hallucinations (generating false information) and outdated knowledge. With RAG, organizations can use powerful language models to work with their own, up-to-date databases without the costly and time-consuming process of retraining models.

This innovation is especially valuable in business applications where information accuracy is critical—from customer service to legal document analysis. RAG systems also make it possible to create intelligent assistants who can answer questions based on the latest company data while maintaining the naturalness and fluency of communication characteristic of advanced language models.

How does AWS approach RAG?

The cloud is a great place to implement RAG, but how does AWS approach this topic? The tech giant treats Retrieval-Augmented Generation (RAG) as a key tool enabling enterprises to use language models in combination with their own knowledge sources. This approach is primarily about increasing the relevance, security, and usability of generated responses by enriching models with up-to-date, contextual organizational data.

AWS provides RAG functionality via the Amazon Bedrock service. This is where you can connect an existing LLM (Large Language Model) with your knowledge base. Bedrock simplifies the entire process, offering ready-made mechanisms for retrieving and enriching responses with current information from organizational data. This allows developers to focus on application logic rather than building integration components from scratch. Amazon Bedrock also supports various models from leading providers, offering flexibility in choosing technology for specific business needs.

Beyond Amazon Bedrock, AWS offers a broad range of supporting services that create a complete ecosystem for implementing RAG systems. An example is Amazon Kendra, a service that provides advanced enterprise search capabilities with built-in connectors to popular data sources such as SharePoint, Confluence, or Amazon S3.

Databases that can power RAG processes are also important. The most commonly used on AWS include Amazon Aurora PostgreSQL-Compatible Edition and Amazon RDS for PostgreSQL with the pgvector plugin. You might also consider Amazon Neptune, which enables relationship analysis and processing of graph and vector data—useful for semantic search and relationship analysis. Amazon DocumentDB, compatible with MongoDB, allows flexible document management and storage, while Amazon MemoryDB provides ultra-fast access to frequently used data thanks to in-memory architecture, supporting quick responses in RAG scenarios.

This way, enterprises get not only tools for building RAG systems but also production-ready infrastructure with the highest security and reliability standards.

Securing RAG implementations with Amazon Bedrock

Using GenAI raises many concerns about data security, especially regarding unauthorized access, encryption, and the use of data for model training. Implementing generative AI with RAG in AWS Cloud relies on the Amazon Bedrock service, which protects data, models, and processes at every stage of the application lifecycle.

When it comes to RAG implementation, AWS offers two main architectural patterns. The first is data redaction at the storage level, where identifying and masking sensitive data takes place before it is stored in the vector knowledge store. Importantly, this method aligns with the Zero Trust approach and reduces the risk of unintentional disclosure of sensitive data. The second pattern is role-based access to sensitive data, where access control is defined based on user roles and permissions. This approach works well in environments where many people perform similar tasks or hold the same position.

Critically, during model fine-tuning, AWS creates a private copy of the base model—customer data is processed only within this isolated model instance, is NOT shared with model providers, and is NOT included in the process of improving base models. The security of this solution is ensured by environment isolation mechanisms, detailed access permission management, and the physical and logical separation of customer data from the rest of the infrastructure. This means that even during advanced operations such as fine-tuning, the confidentiality and integrity of data remain under strict AWS policy protection.

AWS has also developed reference architectures for RAG implementations, which clearly focus on security—primarily through advanced network security measures and access management. Moreover, Amazon Bedrock complies with numerous regulatory programs, making it suitable for healthcare organizations, financial institutions, and enterprises handling sensitive information. It is worth noting that the AWS cloud is based on a shared responsibility model. While AWS is responsible for the hardware layer and offers many tools for creating a secure environment, organizations are responsible for securing the data used within AWS services.

The AWS Cloud also offers many other services dedicated to security. For implementation, it’s worth using AWS Key Management Service (KMS) for encryption key management, allowing you to create, manage, and control your own keys. Basic services such as Amazon S3 for data storage or Amazon CloudWatch for monitoring and log collection can also be very useful.

Can you use RAG technology without choosing Amazon Bedrock? Yes, there is the traditional AI installation on EC2. This approach means full control over the language model, its environment, and the entire infrastructure—from vectorization to the vector database to authentication and scaling mechanisms. This model offers great flexibility but requires advanced technical knowledge and significant resources to manage security, monitoring, and maintenance of the entire stack. As a result, building an AI solution in this way involves a lot of work and responsibility for the development team.

In contrast, the RAG solution on Amazon Bedrock is a ready-made, modular serverless architecture where the foundation model (e.g., Claude, Llama) is automatically enriched with company data thanks to integrated search and prompt enrichment services. AWS provides most of the components, enabling fast deployment and scaling of AI solutions. This allows companies to focus on data and business logic rather than building infrastructure from scratch. AWS’s version of RAG means faster experimentation and deployment, greater security, and a much lower barrier to entry into the world of generative AI.

Summary

The RAG solution may seem like a natural use of available tools, but a closer look shows how incredibly interesting—and conceptually demanding—it can be to combine existing generative AI models with company knowledge resources. Thanks to Amazon Bedrock, the entry threshold for RAG technology is significantly lowered while providing high security, scalability, and convenience. Implementing RAG is a huge technological leap for organizations and companies across various market sectors.

Do you want to implement RAG technology with Amazon Bedrock in your organization? Take advantage of our experts’ help and embrace modern GenAI today! Contact us at kontakt@lcloud.pl.