Sigma Defense to Build SUNet 2.0 Prototype for Defense AI Work

Jul 27, 2026
Sigma Defense has been selected to deliver a SUNet 2.0 prototype for the U.S. Department of War. The six month effort will produce a Minimum Viable Product for AI and machine learning development, data operations, secure collaboration, governance, and technology integration.

Sigma Defense has been selected to deliver a SUNet 2.0 prototype for the U.S. Department of War, the company announced in a press release.

SUNet 2.0, short for Secure Unclassified Network, is planned as a secure and governed environment for artificial intelligence and machine learning development, data collaboration, mission partner integration, technology testing, and future operational mission applications.

Under the initial six month effort, Sigma Defense will deliver a production representative Minimum Viable Product. The prototype is intended to let the government evaluate core capabilities for AI and machine learning development, data operations, secure collaboration, governance, and technology integration.

The prototype is the first phase of the program and may lead to a later production effort. Sigma Defense said its proposed platform is designed to keep government control over governance, identity, security, data, and operations.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like Defense AI Brief or Daily AI Brief.

Also, consider following us on social media:

Defense AI Brief AI Brief AI Brief (X)
Free newsletter

Defense AI Brief

Your weekly intelligence briefing on the technology shaping modern warfare and national security.

Guide
AI Legal Assistants

AI Legal Assistants

Research, drafting, review and knowledge tools for corporate legal teams and transactional firms

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more

You may also like

Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.