Category: AWS Identity and Access Management (IAM)

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).

Amazon Web Services (AWS) Ground Station has many different parameters and options to choose from when creating a contact and setting up data delivery. For this reason, it is advised that infrastructure as code (IaC) is used. This post is the first of a two-part tutorial that provides an overview of the resources required to create AWS CloudFormation templates for Digital Intermediate Frequency (DigIF) workloads, using AWS Ground Station to transmit data between the satellite and AWS Cloud environment.

To improve safety and convenience, transportation agencies amass a substantial volume of data. However, these organizations encounter challenges in data accuracy validation due to issues related to data quality and occasional missing information. With the incorporation of new artificial intelligence and machine learning capabilities from Amazon Web Services (AWS), they can take advantage of no-code solutions to identify and address data gaps.

Credit unions face unique security, regulatory, and compliance obligations. These requirements mean that a sound cybersecurity posture is essential for credit unions across both cloud workloads and on-premises technology. Read this blog post to learn how Amazon Web Services (AWS) can help credit unions prepare for audits, assess security posture, and produce documentation for state or federal regulators.

Northwestern University Libraries’ (NUL) relationship with Amazon Web Services (AWS) helped lead to innovative approaches to NUL’s digital collections suite. Read this post to learn how NUL leveraged an open-source standard and AWS Lambda to make it simpler for researchers to examine, compare, share, and cite images and audio/visual files across libraries.