Category: AWS Identity and Access Management (IAM)

In January 2020, Amazon Web Services (AWS) inaugurated a Cloud Innovation Centre (CIC) at the University of British Columbia (UBC). The CIC uses emerging technologies to solve real-world problems and has produced more than 50 prototypes in sectors like healthcare, education, and research. The Centre’s work has involved 300-plus AWS accounts across various groups, including external collaborators, UBC staff, students, and researchers. This post discusses the management of AWS in higher education institutions, emphasizing governance to securely foster innovation without compromising security and detailing policies and responsibilities for managing AWS accounts across projects and research.

Many Amazon Web Service (AWS) customers in regulated environments such as the U.S. Department of Defense (DoD) struggle to gain security approval to take advantage of the scaling of Amazon Elastic Cloud Compute (Amazon EC2) using its Auto Scaling capabilities. This is often attributed to configuration management, total asset inventory, compliance with agency third-party security tools, and agency authorization documentation. This post provides AWS recommended best practices for implementing EC2 Auto Scaling in DoD environments. 

The Department of the Navy (DoN) modified its blanket purchase agreement (BPA) with Amazon Web Services (AWS) to provide U.S. Navy and Marine Corps Organizations streamlined access to AWS Partners solutions available in AWS Marketplace. AWS Marketplace provides Navy and Marine Corps Organization access to commercial software and services from more than 4,000 trusted providers – accelerating procurement and modernization, improving controls and visibility, and optimizing IT spend.

In this post, we show you how you can push or pull your security telemetry data to the National Cybersecurity Protection System (NCPS) Cloud Log Aggregation Warehouse (CLAW) using Amazon Web Services (AWS) Simple Storage Service (Amazon S3) or third-party solutions.

The Amazon Web Services (AWS) commitment to safe, transparent, and responsible artificial intelligence (AI)—including generative AI—is reflected in our endorsement of the White House Voluntary AI Commitments, our participation in the UK AI Safety Summit, and our dedication to providing customers with features that address specific challenges in this space. In this post, we explore how AWS can help agencies address the governance requirements outlined in the Office of Management and Budget (OMB) memo M-2410 as public sector entities look to build internal capacity for AI.

In this post, we explore the complexities of data privacy and controls on Amazon Web Services (AWS), examine how creating a landing zone within which to contain such data is important, and highlight the differences between creating a landing zone from scratch compared with using the AWS Landing Zone Accelerator (LZA) for Healthcare. To aid explanation, we use a simple healthcare workload as an example. We also explain how LZA for Healthcare codifies HIPAA controls and AWS Security Best Practices to accelerate the creation of an environment to run protective health information workloads in AWS.

The public sector’s use of Internet of Things (IoT) devices is steadily growing, as these organizations learn how to implement and derive value from IoT solutions. Public sector agencies and organizations deploy IoT devices in a variety of areas, such as transportation and infrastructure, crime prevention, education, and utilities and environment. In this post, we are going to use the Cybersecurity and Infrastructure Security Agency ‘s (CISA) guidelines as a reference to improve the security of your IoT devices and learn how to address vulnerabilities using Amazon Web Services (AWS) IoT services.

Government agencies are increasingly using large language models (LLMs) powered by generative artificial intelligence (AI) to extract valuable insights from their data in the Amazon Web Services (AWS) GovCloud (US) Regions. In this guide, we walk you through the process of hosting LLMs on Amazon Elastic Compute Cloud (Amazon EC2) instances, using the Hugging Face Text Generation Inference (TGI) Container (TGI) for serving custom LLMs.

The Association of Schools and Programs of Public Health (ASPPH) — a nonprofit association with a vision for improved health and well-being for everyone, everywhere — partnered with Amazon Web Services (AWS) Professional Services (AWS ProServe) to move their curated data to a managed data lake on AWS. In this blog post, we share how ASPPH and AWS designed and built the data lake and the results of moving to a modern, scalable data architecture.

Healthcare authorities and providers use state-run controlled substance databases (CSDs) to track prescriptions and identify patients for substance abuse. CSDs help evaluate treatment options, screen patients who may be at risk for drug abuse problems, and make informed decisions about prescribing medication. This post explains how healthcare authorities can leverage CSD data to enhance their decision-making processes within business operations by using Amazon Web Services (AWS).