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.
Digital IDs can improve convenience, but they risk surveillance, data misuse, and exclusion if not designed with privacy, security, and accessibility safeguards.
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.
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.
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.
