Category: HAQM Bedrock Guardrails

In this post, we explore two approaches for securing sensitive data in RAG applications using HAQM Bedrock. The first approach focused on identifying and redacting sensitive data before ingestion into an HAQM Bedrock knowledge base, and the second demonstrated a fine-grained RBAC pattern for managing access to sensitive information during retrieval. These solutions represent just two possible approaches among many for securing sensitive data in generative AI applications.

In this post, we combine HAQM Bedrock Agents and Foursquare APIs to demonstrate how you can use a location-aware agent to bring personalized responses to your users.

This post describes how the AWS Customer Channel Technology – Localization Team worked with TransPerfect to integrate HAQM Bedrock into the GlobalLink translation management system, a cloud-based solution designed to help organizations manage their multilingual content and translation workflows. Organizations use TransPerfect’s solution to rapidly create and deploy content at scale in multiple languages using AI.

In this post, we explore practical strategies for using Constitutional AI to produce compliant content efficiently and effectively using HAQM Bedrock and LangGraph to build ConstitutionalChain for rapid content creation in highly regulated industries like finance and healthcare

To improve factual accuracy of large language model (LLM) responses, AWS announced HAQM Bedrock Automated Reasoning checks (in gated preview) at AWS re:Invent 2024. In this post, we discuss how to help prevent generative AI hallucinations using HAQM Bedrock Automated Reasoning checks.

HAQM Bedrock Guardrails announces the general availability of image content filters, enabling you to moderate both image and text content in your generative AI applications. In this post, we discuss how to get started with image content filters in HAQM Bedrock Guardrails.

Today, we’re announcing a significant enhancement to HAQM Bedrock Guardrails: AWS Identity and Access Management (IAM) policy-based enforcement. This powerful capability enables security and compliance teams to establish mandatory guardrails for every model inference call, making sure organizational safety policies are consistently enforced across AI interactions. This feature enhances AI governance by enabling centralized control over guardrail implementation.

Healthcare decisions often require integrating information from multiple sources, such as medical literature, clinical databases, and patient records. LLMs lack the ability to seamlessly access and synthesize data from these diverse and distributed sources. This limits their potential to provide comprehensive and well-informed insights for healthcare applications. In this blog post, we will explore how Mistral LLM on HAQM Bedrock can address these challenges and enable the development of intelligent healthcare agents with LLM function calling capabilities, while maintaining robust data security and privacy through HAQM Bedrock Guardrails.

In this post, we explore how Automated Reasoning checks work through various common FSI scenarios such as insurance legal triaging, underwriting rules validation, and claims processing.

AWS and Appian’s collaboration marks a significant advancement in business process automation. By using the power of HAQM Bedrock and Anthropic’s Claude models, Appian empowers enterprises to optimize and automate processes for greater efficiency and effectiveness. This blog post will cover how Appian AI skills build automation into organizations’ mission-critical processes to improve operational excellence, reduce costs, and build scalable solutions.