Government leaders discuss how to ensure seamless access to public benefits through breaking down silos, user-friendly digital identities, and privacy-focused security measures.
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.
A policy directive that establishes standards and guidance for federal executive agencies to manage, secure, and deliver public websites and digital services that are user-centered, accessible, and data-driven.
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.
The NIST Risk Management Framework (RMF) Introductory Courses offer free, self-paced online training on managing cybersecurity and privacy risks using NIST’s RMF methodology and related publications.
National Institute of Standards and Technology (NIST)
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.
