Category: Amazon Bedrock Guardrails

We’re excited to announce new security capabilities in Datadog Cloud Security that can help you detect and remediate Amazon Bedrock misconfigurations before they become security incidents. This integration helps organizations embed robust security controls and secure their use of the powerful capabilities of Amazon Bedrock by offering three critical advantages: holistic AI security by integrating AI security into your broader cloud security strategy, real-time risk detection through identifying potential AI-related security issues as they emerge, and simplified compliance to help meet evolving AI regulations with pre-built detections.

University Startups, headquartered in Bethesda, MD, was founded in 2020 to empower high school students to expand their education beyond a traditional curriculum. University Startups is focused on special education and related services in school districts throughout the US. In this post, we explain how University Startups uses generative AI technology on AWS to enable students to design a specific plan for their future either in education or the work force.

In this post, we examine a solution adopted by Nippon Life India Asset Management Limited that improves the accuracy of the response over a regular (naive) RAG approach by rewriting the user queries and aggregating and reranking the responses. The proposed solution uses enhanced RAG methods such as reranking to improve the overall accuracy

PayU offers a full-stack digital financial services system that serves the financial needs of merchants, banks, and consumers through technology. In this post, we explain how we equipped the PayU team with an enterprise AI solution and democratized AI access using Amazon Bedrock, without compromising on data residency requirements.

In the fashion industry, teams are frequently innovating quickly, often utilizing AI. Sharing content, whether it be through videos, designs, or otherwise, can lead to content moderation challenges. There remains a risk (through intentional or unintentional actions) of inappropriate, offensive, or toxic content being produced and shared. In this post, we cover the use of the multimodal toxicity detection feature of Amazon Bedrock Guardrails to guard against toxic content. Whether you’re an enterprise giant in the fashion industry or an up-and-coming brand, you can use this solution to screen potentially harmful content before it impacts your brand’s reputation and ethical standards. For the purposes of this post, ethical standards refer to toxic, disrespectful, or harmful content and images that could be created by fashion designers.

In this post, we introduce the new safeguard tiers available in Amazon Bedrock Guardrails, explain their benefits and use cases, and provide guidance on how to implement and evaluate them in your AI applications.

In this post, we demonstrate how to use Amazon Bedrock and LangGraph to build a personalized customer support experience for an ecommerce retailer. By integrating the Mistral Large 2 and Pixtral Large models, we guide you through automating key customer support workflows such as ticket categorization, order details extraction, damage assessment, and generating contextual responses.

In this post, we demonstrate how Amazon Bedrock Guardrails helps block harmful and undesirable multimodal content. Using a healthcare insurance call center scenario, we walk through the process of configuring and testing various guardrails.

In this post, we demonstrate how to use the Open AI Whisper foundation model (FM) Whisper Large V3 Turbo, available in Amazon Bedrock Marketplace, which offers access to over 140 models through a dedicated offering, to produce near real-time transcription. These transcriptions are then processed by Amazon Bedrock for summarization and redaction of sensitive information.

HERE collaborated with the GenAIIC. Our joint mission was to create an intelligent AI coding assistant that could provide explanations and executable code solutions in response to users’ natural language queries. The requirement was to build a scalable system that could translate natural language questions into HTML code with embedded JavaScript, ready for immediate rendering as an interactive map that users can see on screen.