A summer of security: empowering cyber defenders with AI
Here’s what we’re announcing at cybersecurity conferences like Black Hat USA and DEF CON 33.
Congratulations to the MSRC 2025 Most Valuable Security Researchers!
The Microsoft Researcher Recognition Program offers public thanks and recognition to security researchers who help protect our customers through discovering and sharing security vulnerabilities under Coordinated Vulnerability Disclosure. Today, we are excited to recognize this year’s 100 Most Valuable Researchers (MVRs), based on the total number of points earned for each valid report.
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 …
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, …
Build secure RAG applications with AWS serverless data lakes
Data is your generative AI differentiator, and successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Traditional data architectures often struggle to meet the unique demands of generative such as applications. An effective generative AI data strategy requires several key components like seamless integration of diverse data sources, …
Build secure RAG applications with AWS serverless data lakes Read More »
Try featured notebooks on selected topics in NotebookLM
NotebookLM is adding featured notebooks, with works selected by partners like The Atlantic and The Economist.
Advanced fine-tuning methods on Amazon SageMaker AI
This post provides the theoretical foundation and practical insights needed to navigate the complexities of LLM development on Amazon SageMaker AI, helping organizations make optimal choices for their specific use cases, resource constraints, and business objectives. We also address the three fundamental aspects of LLM development: the core lifecycle stages, the spectrum of fine-tuning methodologies, …
Advanced fine-tuning methods on Amazon SageMaker AI Read More »
Streamline machine learning workflows with SkyPilot on Amazon SageMaker HyperPod
This post is co-written with Zhanghao Wu, co-creator of SkyPilot. The rapid advancement of generative AI and foundation models (FMs) has significantly increased computational resource requirements for machine learning (ML) workloads. Modern ML pipelines require efficient systems for distributing workloads across accelerated compute resources, while making sure developer productivity remains high. Organizations need infrastructure solutions …
Streamline machine learning workflows with SkyPilot on Amazon SageMaker HyperPod Read More »
Intelligent document processing at scale with generative AI and Amazon Bedrock Data Automation
Extracting information from unstructured documents at scale is a recurring business task. Common use cases include creating product feature tables from descriptions, extracting metadata from documents, and analyzing legal contracts, customer reviews, news articles, and more. A classic approach to extracting information from text is named entity recognition (NER). NER identifies entities from predefined categories, …
Build a conversational data assistant, Part 2 – Embedding generative business intelligence with Amazon Q in QuickSight
In Part 1 of this series, we explored how Amazon’s Worldwide Returns & ReCommerce (WWRR) organization built the Returns & ReCommerce Data Assist (RRDA)—a generative AI solution that transforms natural language questions into validated SQL queries using Amazon Bedrock Agents. Although this capability improves data access for technical users, the WWRR organization’s journey toward truly …