Category: Amazon Bedrock Guardrails

In this post, we walk you through how to implement a fully automated, context-aware AI solution using a serverless architecture on AWS. This solution helps organizations looking to deploy responsible AI systems, align with compliance requirements for vulnerable populations, and help maintain appropriate and trustworthy AI responses across diverse user groups without compromising performance or governance.

This post shows you how to build an AI-powered A/B testing engine using Amazon Bedrock, Amazon Elastic Container Service, Amazon DynamoDB, and the Model Context Protocol (MCP). The system improves traditional A/B testing by analyzing user context  to make smarter variant assignment decisions during the experiment.

In this post, we will show you how to configure Amazon Bedrock Guardrails for efficient performance, implement best practices to protect your applications, and monitor your deployment effectively to maintain the right balance between safety and user experience.

In this post, we walk through how global cross-Region inference routes requests and where your data resides, then show you how to configure the required AWS Identity and Access Management (IAM) permissions and invoke Claude 4.5 models using the global inference profile Amazon Resource Name (ARN). We also cover how to request quota increases for your workload. By the end, you’ll have a working implementation of global cross-Region inference in af-south-1.

This post introduces generative AI guided business reporting—with a focus on writing achievements & challenges about your business—providing a smart, practical solution that helps simplify and accelerate internal communication and reporting.

In this post, we demonstrate how you can address these challenges by adding centralized safeguards to a custom multi-provider generative AI gateway using Amazon Bedrock Guardrails.

This post shows an automated PII detection and redaction solution using Amazon Bedrock Data Automation and Amazon Bedrock Guardrails through a use case of processing text and image content in high volumes of incoming emails and attachments. The solution features a complete email processing workflow with a React-based user interface for authorized personnel to more securely manage and review redacted email communications and attachments. We walk through the step-by-step solution implementation procedures used to deploy this solution. Finally, we discuss the solution benefits, including operational efficiency, scalability, security and compliance, and adaptability.

In this post, we demonstrate how to implement a predictive maintenance solution using Foundation Models (FMs) on Amazon Bedrock, with a case study of Amazon’s manufacturing equipment within their fulfillment centers. The solution is highly adaptable and can be customized for other industries, including oil and gas, logistics, manufacturing, and healthcare.

Amazon Bedrock Guardrails now extends its safety controls to protect code generation across twelve programming languages, addressing critical security challenges in AI-assisted software development. In this post, we explore how to configure content filters, prompt attack detection, denied topics, and sensitive information filters to safeguard against threats like prompt injection, data exfiltration, and malicious code generation while maintaining developer productivity .

Enterprises in regulated industries often need mathematical certainty that every AI response complies with established policies and domain knowledge. Regulated industries can’t use traditional quality assurance methods that test only a statistical sample of AI outputs and make probabilistic assertions about compliance. When we launched Automated Reasoning checks in Amazon Bedrock Guardrails in preview at […]