Artificial Intelligence

Category: Security, Identity, & Compliance

Securing Amazon Bedrock Agents: A guide to safeguarding against indirect prompt injections

Generative AI tools have transformed how we work, create, and process information. At Amazon Web Services (AWS), security is our top priority. Therefore, Amazon Bedrock provides comprehensive security controls and best practices to help protect your applications and data. In this post, we explore the security measures and practical strategies provided by Amazon Bedrock Agents to safeguard your AI interactions against indirect prompt injections, making sure that your applications remain both secure and reliable.

WordFinder app: Harnessing generative AI on AWS for aphasia communication

In this post, we showcase how Dr. Kori Ramajoo, Dr. Sonia Brownsett, Prof. David Copland, from QARC, and Scott Harding, a person living with aphasia, used AWS services to develop WordFinder, a mobile, cloud-based solution that helps individuals with aphasia increase their independence through the use of AWS generative AI technology.

Scenario 1 - Retrieval Flow

Protect sensitive data in RAG applications with Amazon Bedrock

In this post, we explore two approaches for securing sensitive data in RAG applications using Amazon Bedrock. The first approach focused on identifying and redacting sensitive data before ingestion into an Amazon 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.

Build a FinOps agent using Amazon Bedrock with multi-agent capability and Amazon Nova as the foundation model

Build a FinOps agent using Amazon Bedrock with multi-agent capability and Amazon Nova as the foundation model

In this post, we use the multi-agent feature of Amazon Bedrock to demonstrate a powerful and innovative approach to AWS cost management. By using the advanced capabilities of Amazon Nova FMs, we’ve developed a solution that showcases how AI-driven agents can revolutionize the way organizations analyze, optimize, and manage their AWS costs.

Maximize your file server data’s potential by using Amazon Q Business on Amazon FSx for Windows

In this post, we show you how to connect Amazon Q, a generative AI-powered assistant, to Amazon FSx for Windows File Server to securely analyze, query, and extract insights from your file system data.

Virtual Meteorologist Featured Image

Building a virtual meteorologist using Amazon Bedrock Agents

In this post, we present a streamlined approach to deploying an AI-powered agent by combining Amazon Bedrock Agents and a foundation model (FM). We guide you through the process of configuring the agent and implementing the specific logic required for the virtual meteorologist to provide accurate weather-related responses.

The following diagram illustrates the workflow of patch-level prediction tasks on a WSI

Accelerate digital pathology slide annotation workflows on AWS using H-optimus-0

In this post, we demonstrate how to use H-optimus-0 for two common digital pathology tasks: patch-level analysis for detailed tissue examination, and slide-level analysis for broader diagnostic assessment. Through practical examples, we show you how to adapt this FM to these specific use cases while optimizing computational resources.

Security best practices to consider while fine-tuning models in Amazon Bedrock

In this post, we implemented secure fine-tuning jobs in Amazon Bedrock, which is crucial for protecting sensitive data and maintaining the integrity of your AI models. By following the best practices outlined in this post, including proper IAM role configuration, encryption at rest and in transit, and network isolation, you can significantly enhance the security posture of your fine-tuning processes.

Video security analysis for privileged access management using generative AI and Amazon Bedrock

In this post, we show you an innovative solution to a challenge faced by security teams in highly regulated industries: the efficient security analysis of vast amounts of video recordings from Privileged Access Management (PAM) systems. We demonstrate how you can use Anthropic’s Claude 3 family of models and Amazon Bedrock to perform the complex task of analyzing video recordings of server console sessions and perform queries to highlight any potential security anomalies.