Category: HAQM Bedrock

HAQM Web Services (AWS) is pleased to announce the release of new Security Reference Architecture (SRA) code examples for securing generative AI workloads. The examples include two comprehensive capabilities focusing on secure model inference and RAG implementations, covering a wide range of security best practices using AWS generative AI services. These new code examples are […]

April 9, 2025: We updated content about HAQM Bedrock Guardrails to cover the recently added condition key bedrock:GuardrailIdentifier. March 27, 2025: Two policies in this post were updated. Generative AI applications often involve a combination of various services and features—such as HAQM Bedrock and large language models (LLMs)—to generate content and to access potentially confidential […]

In part 1 of this blog series, we walked through the risks associated with using sensitive data as part of your generative AI application. This overview provided a baseline of the challenges of using sensitive data with a non-deterministic large language model (LLM) and how to mitigate these challenges with HAQM Bedrock Agents. The next […]

Retrieval-Augmented Generative (RAG) applications enhance the responses retrieved from large language models (LLMs) by integrating external data such as downloaded files, web scrapings, and user-contributed data pools. This integration improves the models’ performance by adding relevant context to the prompt. While RAG applications are a powerful way to dynamically add additional context to an LLM’s prompt […]

April 3, 2025: We’ve updated this post to reflect the new 2025 OWASP top 10 for LLM entries. This is part 1 of a two-part blog series. See part 2. Data security and data authorization, as distinct from user authorization, is a critical component of business workload architectures. Its importance has grown with the evolution […]

April 3, 2025: We’ve updated this post to reflect the new 2025 OWASP top 10 for LLM entries. Customers are finding several advantages to using generative AI within their applications. However, using generative AI adds new considerations when reviewing the threat model of an application, whether you’re using it to improve the customer experience for […]

Generative AI–based applications have grown in popularity in the last couple of years. Applications built with large language models (LLMs) have the potential to increase the value companies bring to their customers. In this blog post, we dive deep into network perimeter protection for generative AI applications. We’ll walk through the different areas of network […]

Mitigate risks like data exposure, model exploits, and ethical lapses when deploying HAQM Bedrock chatbots. Implement guardrails, encryption, access controls, and governance frameworks.

Have you ever pondered the intricate workings of generative artificial intelligence (AI) models, especially how they process and generate responses? At the heart of this fascinating process lies the context window, a critical element determining the amount of information an AI model can handle at a given time. But what happens when you exceed the […]

Generative artificial intelligence (AI) has captured the imagination of organizations and individuals around the world, and many have already adopted it to help improve workforce productivity, transform customer experiences, and more. When you use a generative AI-based service, you should understand how the information that you enter into the application is stored, processed, shared, and […]