Digital IDs can improve convenience, but they risk surveillance, data misuse, and exclusion if not designed with privacy, security, and accessibility safeguards.
This framework provides practical guidance, detailed reference designs, and example solutions to help organizations securely adopt and operationalize Zero Trust principles across diverse IT environments.
National Institute of Standards and Technology (NIST)
The Digital Benefit Network's Digital Identity Community of Practice held a session to hear considerations from civil rights technologists and human-centered design practitioners on ways to ensure program security while simultaneously promoting equity, enabling accessibility, and minimizing bias.
This report analyzes the growing use of generative AI, particularly large language models, in enabling and scaling fraudulent activities, exploring the evolving tactics, risks, and potential countermeasures.
This is a government catalog of reusable digital service components, templates, and patterns designed to help public sector teams build services more efficiently and consistently.
This page describes how the GSA’s 10x team supported digital trust and security across federal services by building shared tools, automating compliance, and exploring privacy-preserving techniques.
The article highlights the growing issue of SNAP benefit theft through skimming and advocates for permanent security measures and benefit replacements to protect vulnerable households.
Government leaders discuss how to ensure seamless access to public benefits through breaking down silos, user-friendly digital identities, and privacy-focused security measures.
This playbook provides government-wide guidance for planning, procuring, and managing digital, data, and technology (DDaT) projects with a focus on innovation, agile delivery, cybersecurity, sustainability, and commercial best practices.
This article advises government agencies to prioritize cybersecurity methods over AI-driven approaches when combating identity fraud in benefits programs, highlighting potential risks that automated systems pose to legitimate applicants.
