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2024

Aligning AI Strategies: A Look at the Executive Order Section 10

Over the past few years, artificial intelligence (AI) has been a forefront technology within the federal government’s operations. Now, a fresh narrative has emerged―Section 10 of the Executive Order (EO) on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence directs federal agencies on developing and using AI uniformly in their activities.

Cloud-to-Edge AI Is a Decision-Placement Problem

Originally published in 2024; substantially revised in 2026 to deepen the analysis and incorporate additional sources.

“Cloud to edge” is often presented as an infrastructure continuum: train and aggregate centrally, then move inference closer to users and sensors. That description is technically sound but strategically incomplete.

The important design question is not where the compute happens. It is where data becomes knowledge, where a recommendation becomes a decision, and which parts of that chain must continue to function when connectivity, latency, power, or trust changes.

Cloud-to-edge AI is a decision-placement problem expressed through architecture.

The NIST AI RMF Is an Operating Model, Not a Checklist

The Federal Artificial Intelligence Risk Management Act of 2024 proposed requiring federal agencies to use the National Institute of Standards and Technology’s AI Risk Management Framework. The idea was sensible and bipartisan: federal AI should be governed through a common, credible risk-management structure rather than a patchwork of improvised agency practices.

The danger is equally familiar. Institutions can “adopt” a framework by mapping its language into policy, completing templates, and producing inventories while leaving the decisions that determine AI risk largely unchanged.

The NIST AI Risk Management Framework is most valuable when treated as an operating model: a way to connect mission context, evidence, authority, delivery, monitoring, and accountability across the lifecycle of a system.

Federal AI Procurement Is a Risk-Management Control Plane

The Federal Artificial Intelligence Risk Management Act of 2024 proposed more than agency adoption of the NIST AI Risk Management Framework. It also contemplated acquisition support and contract language that would carry risk-management expectations into federal purchasing.

That may be the proposal’s most operationally important feature.

Federal agencies build some AI systems themselves, but they increasingly acquire models, cloud services, data, integrations, and AI-enabled products from vendors. If governance obligations stop at the agency boundary, agencies can remain publicly accountable for systems whose material evidence and operational control sit inside private contracts.

Procurement is therefore not an administrative step after AI policy. It is the control plane through which public accountability becomes enforceable across the supply chain.

Civil-Rights Risk Lives in the Decision System, Not Only the Model

The Department of Justice’s January 2024 interagency convening on artificial intelligence and civil rights reflected an important federal position: existing civil-rights and consumer-protection laws continue to apply when decisions are mediated by algorithms.

That proposition is necessary. Its technical consequence deserves equal emphasis.

Discrimination rarely resides in a single model parameter or fairness metric. It can enter through the choice of problem, the collection of data, a proxy variable, the design of an interface, unequal access to a digital service, the discretion granted to staff, or the absence of a practical appeal. An AI system can satisfy a narrow statistical test and still participate in an unlawful or inequitable decision process.

Civil-rights analysis must therefore treat the whole decision system as the unit of accountability.

Early 2024: Senate Commerce Chair Cantwell to Introduce Comprehensive AI Legislation

A New Stance on AI: Insights into Senate Commerce Chair Cantwell’s Upcoming Legislation

The path to a comprehensive artificial intelligence (AI) legislation is set to take a significant turn, thanks to initiatives by Senate Commerce Committee Chair, Maria Cantwell. According to sources cited by FedScoop, Cantwell, a Democrat from Washington, is expected to introduce an array of bipartisan bills tackling various facets of AI.

Project Linchpin and the Architecture of Operational AI

Originally published in 2024; substantially revised in 2026 to deepen the analysis and incorporate additional sources.

The Army’s Project Linchpin is significant for a reason that extends beyond any individual model or sensor use case. It treats artificial intelligence as a continuously operated capability system: data is prepared, models are trained and evaluated, software is integrated, deployments are observed, and operational feedback informs the next release.

That sounds familiar to anyone who has built a mature software or machine-learning platform. Inside a defense acquisition environment, however, it represents a substantial change in what the government is actually buying and governing.

The unit of acquisition is no longer only the algorithm. It is the trusted pipeline through which algorithms become—and remain—operational capabilities.

Reevaluating Strategies: Navy’s 4th Fleet Highlights the Need for Command Center Upgrades for Unmanned Operations

Reevaluating Strategies: Navy’s 4th Fleet Highlights the Need for Command Center Upgrades for Unmanned Operations

Unmanned systems command has certainly been a focal point for the Navy’s 4th Fleet, but as these technologies advance, the challenge of managing them effectively grows. Rear Adm. Jim Aiken recently shared insights into the progress and pitfalls experienced in combatting this issue.

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