In a noteworthy initiative towards bolstering privacy-preserving technologies, the U.S. Department of Energy (DoE) has joined hands with the National Science Foundation (NSF) to establish a new Research Coordination Network (RCN). Designed to support the research, development, and production of Privacy-enhancing Technologies (PETs), this initiative is a part of the governmental measures under the executive order for the responsible and trustworthy use of artificial intelligence (AI).
In a bid to advance its military asset identification capabilities, Preligens, a Paris-based firm, is now seeking to process video footage captured by unmanned aerial vehicles (UAVs). The objective is to detect assets deployed by Russia and China more effectively.
Revised and substantially expanded July 17, 2026. Executive Order 14110, discussed as the historical catalyst for the original post, was revoked in January 2025; the operating lessons remain relevant to later federal AI policy.
In February 2024, federal officials described early progress implementing President Biden's executive order on artificial intelligence. The Office of Science and Technology Policy (OSTP) occupied a central coordinating role, and the public conversation emphasized completed deadlines, forthcoming guidance, and agencies “leading by example.”
The original version of this post reproduced that progress narrative without interrogating its unit of measure. It treated executive action, policy development, and agency implementation as though they were successive names for the same thing.
They are not.
A mandate can be issued but not translated. A deliverable can be published but not adopted. A process can be adopted but fail to change technical behavior. A system can be deployed but fail to improve a public outcome. Each transition requires different evidence and different ownership.
The management problem is therefore one of implementation traceability: can leaders follow a policy intent all the way through accountable deliverables, changed operating practices, fielded capabilities, and measured outcomes—and can they see where that chain breaks?
Virginia Senator Mark Warner recognizes the worth of artificial intelligence (AI) but articulates concerns about possible adverse effects on federal programs, electoral processes, and financial markets without appropriate controls in place.
Detroit-based institute, LIFT, has initiated a project led by aerospace and defense company, RTX, aimed at developing a tool to model the emergence of cracks and damage within tantalum-coated gun barrels. This would enable improved life predictions. The project, termed “Life Prediction of Tantalum-Coated Gun Barrels Under Fast Thermal Transient Conditions”, is one of six selected for funding under the LIFT Ecosystem Accelerator Program. The original article can be found here .
Breaking Defense is hosting its inaugural live webinar to delve into the exciting potential of Generative AI in the national defense sector. The focus will include discussion on how the Department of Defense (DoD) can effectively adapt and leverage AI capabilities to tackle major threats from Great Powers in pivotal domains such as cybersecurity and missile proliferation.
With the initiation of House task force work on artificial intelligence (AI), Oregon Democrat Rep. Suzanne Bonamici expresses her concerns and focuses on AI in a Q&A session with FedScoop. The task force, to which Bonamici has been appointed, will address crucial issues such as bias, consent, discrimination, and privacy in the context of AI.
In an age of escalating cybersecurity threats, government agencies are beginning to recognize the tremendous value of air-gapped, immutable backups with isolated recovery. This robust approach to data protection can ensure the continuity of operations even when primary systems are compromised.