How Rapid7 automates vulnerability risk scores with ML pipelines using Amazon SageMaker AI

This post is cowritten with Jimmy Cancilla from Rapid7. Organizations are managing increasingly distributed systems, which span on-premises infrastructure, cloud services, and edge devices. As systems become interconnected and exchange data, the potential pathways for exploitation multiply, and vulnerability management becomes critical to managing risk. Vulnerability management (VM) is the process of identifying, classifying, prioritizing,…

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Build AI-driven policy creation for vehicle data collection and automation using Amazon Bedrock

Vehicle data is critical for original equipment manufacturers (OEMs) to drive continuous product innovation and performance improvements and to support new value-added services. Similarly, the increasing digitalization of vehicle architectures and adoption of software-configurable functions allow OEMs to add new features and capabilities efficiently. Sonatus’s Collector AI and Automator AI products address these two aspects…

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AI ‘Nudify’ Websites Are Raking in Millions of Dollars

For years, so-called “nudify” apps and websites have mushroomed online, allowing people to create nonconsensual and abusive images of women and girls, including child sexual abuse material. Despite some lawmakers and tech companies taking steps to limit the harmful services, every month, millions of people are still accessing the websites, and the sites’ creators may…

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Uphold ethical standards in fashion using multimodal toxicity detection with Amazon Bedrock Guardrails

The global fashion industry is estimated to be valued at $1.84 trillion in 2025, accounting for approximately 1.63% of the world’s GDP (Statista, 2025). With such massive amounts of generated capital, so too comes the enormous potential for toxic content and misuse. In the fashion industry, teams are frequently innovating quickly, often utilizing AI. Sharing…

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Fraud detection empowered by federated learning with the Flower framework on Amazon SageMaker AI

Fraud detection remains a significant challenge in the financial industry, requiring advanced machine learning (ML) techniques to detect fraudulent patterns while maintaining compliance with strict privacy regulations. Traditional ML models often rely on centralized data aggregation, which raises concerns about data security and regulatory constraints. Fraud cost businesses over $485.6 billion in 2023 alone, according…

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Long-running execution flows now supported in Amazon Bedrock Flows in public preview

Today, we announce the public preview of long-running execution (asynchronous) flow support within Amazon Bedrock Flows. With Amazon Bedrock Flows, you can link foundation models (FMs), Amazon Bedrock Prompt Management, Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, and other AWS services together to build and scale predefined generative AI workflows. As customers…

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Implement user-level access control for multi-tenant ML platforms on Amazon SageMaker AI

Managing access control in enterprise machine learning (ML) environments presents significant challenges, particularly when multiple teams share Amazon SageMaker AI resources within a single Amazon Web Services (AWS) account. Although Amazon SageMaker Studio provides user-level execution roles, this approach becomes unwieldy as organizations scale and team sizes grow. Refer to the Operating model whitepaper for…

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