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White House drastically shortens deadline for dropping quantum-vulnerable crypto

23 June 2026 at 22:30

The White House is drastically shortening the deadline for government agencies and organizations to adopt new quantum-resistant encryption systems that will withstand attacks that use quantum computers, as the federal government seeks to protect decadesโ€™ worth of secrets belonging to militaries, banks, governments, and most individuals on Earth.

The executive order, titled Securing the Nation against Advanced Cryptographic Attacks, requires computing systems for โ€œhigh-value assetsโ€ and โ€œhigh-impact systemsโ€ to transition to post-quantum cryptographic key establishment schemes by December 31, 2030, and to quantum-safe digital signature schemes by December 31, 2031.

Heading off a significant threat

The new deadline, which for many organizations is about five years sooner than the previous one, comes on the heels of recent research showing that the resources and cost for building a cryptographically relevant quantum computer are far less than previous consensus estimates. In response, Google, Cloudflare, and other companies recently tightened their timelines for moving off vulnerable systems to 2029.

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Build an AI Scientist for Life Science Discovery with NVIDIA BioNeMo Agent Toolkit

23 June 2026 at 13:30
AI scientists are emerging as a new interface for scientific computing. These agents can read papers, write code, generate hypotheses, call APIs, inspect files,...

AI scientists are emerging as a new interface for scientific computing. These agents can read papers, write code, generate hypotheses, call APIs, inspect files, and iterate on results. But science isnโ€™t software engineering. There is no test suite that turns green when a hypothesis is correct; discovery is iterative, uncertain, and grounded in the physical world. You canโ€™t take a general codingโ€ฆ

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Enable Real-Time AI for High-Speed Data Acquisition with DAQIRI

22 June 2026 at 15:00
When AlphaFold2 revolutionized drug discovery in 2020, its success relied entirely on the roughly 170,000 protein structures collected by scientists since 1971...

When AlphaFold2 revolutionized drug discovery in 2020, its success relied entirely on the roughly 170,000 protein structures collected by scientists since 1971 and preserved in the Protein Data Bank. Measured data is the backbone for all AI models and workflows that process data as itโ€™s created, act on what matters in real time, and analyzes data for deep insights. With the current rise of modernโ€ฆ

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Inside NVIDIA Halos for Robotics: A Full-Stack Functional Safety System for Physical AI

Physical AIโ€”robots working autonomously alongside people in factories, warehouses, hospitals, and homesโ€”is arriving faster than most expected. Traditional...

Physical AIโ€”robots working autonomously alongside people in factories, warehouses, hospitals, and homesโ€”is arriving faster than most expected. Traditional safety which was built for structured environments can not work anymore as the spaces become more unstructured and robots move out of cages. AI-driven safety is the key. Marking a major milestone in the arrival of physical AIโ€ฆ

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One-Click Multi-Tenant Security withย  NVIDIA Quantum InfiniBand

11 June 2026 at 19:52
NVIDIA Quantum InfiniBand now offers intent-based security profiles in Unified Fabric Manager (UFM) that enable multi-tenant fabric security in a single...

NVIDIA Quantum InfiniBand now offers intent-based security profiles in Unified Fabric Manager (UFM) that enable multi-tenant fabric security in a single click. NVIDIA Quantum InfiniBand supports three profiles: General, Bare Metal Cloud, and Secured Bare Metal Cloud. Network administrators can now auto-configure: This cuts deployment time to minutes from hours or daysโ€ฆ

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Model Quantization: Turn FP8 Checkpoints into High-Performance Inference Engines with NVIDIA TensorRT

9 June 2026 at 18:27
Decorative image.This post is the third of a three-part series. See also Model Quantization: Concepts, Methods, and Why It Matters and Model Quantization: Post-Training...Decorative image.

This post is the third of a three-part series. See also Model Quantization: Concepts, Methods, and Why It Matters and Model Quantization: Post-Training Quantization Using NVIDIA Model Optimizer. Converting a quantized checkpoint into an NVIDIA TensorRT engine bridges the gap between model optimization and production deployment, enabling faster inference, higher throughputโ€ฆ

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Accelerating Federated Learning Research with AI Agents and NVIDIA FLARE Auto-FL

9 June 2026 at 16:35
Federated learning (FL) research often begins with a deceptively simple question: What should we try next? A new aggregation rule, a FedProx coefficient, a...

Federated learning (FL) research often begins with a deceptively simple question: What should we try next? A new aggregation rule, a FedProx coefficient, a server optimizer setting, a SCAFFOLD variant, or a model architecture tweak may all look promising before an experiment starts. After the run finishes, the harder questions begin: Did the change actually improve the metric?

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How to get operational data off the factory floor without creating an IT breach

Aerial top-down view of a sci-fi industrial factory interior with yellow directional arrows, pink and silver pipes, steel scaffolding walkways, and dramatic blue-grey lighting.

Informational and operational technology data have long been treated as separate domains.

But AI changed the game. Today, you need the capacity to regularly ingest OT data into your IT systems without a hitch. (Or a breach.) You risk being left behind as your competitors put all their data to work, or assume the risk of consistently importing data from the edge to your internal systems.ย 

This problem is an immediate one for any company unwilling to be left behind in the AI era: If you want to take full advantage of AI, you need quick, ready access to relevant data. And if your physical operations have hit a snag, your digital tools need to be kept in the loop regularly.

The solution is not to build a host of custom scripts or depend on legacy FTP or SFTP solutions to bring data in from the edge. Those disparate tools can degrade, leak data, and fail during later, repeated OT data extraction runs.

Instead, engineers looking to free IT and OT data from their respective siloes are turning to a managed solution that offers strong encryption, continuous transfer monitoring, and the ability to fully audit every data handoff across the pipeline

Even more, OT systems โ€” the Programmable Logic Controllers, Supervisory Control and Data Acquisition platforms, and historian databases running protocols like Modbus and OPC UA โ€” were designed for uptime rather than connectivity. In modern architecture, however, no operational data can be left behind.

Getting data out of these environments means working against a connectivity model that was never meant to support the polling frequency or authentication patterns that modern IT infrastructure expects. Adding to the challenge, the more tools you introduce to free the OT data, the more attack vectors they may open.

A breach at the OT boundary can affect the physical systems those networks control. Thatโ€™s a risk calculus most IT security frameworks werenโ€™t built to handle.

On at 12 p.m. Eastern/9 a.m. On Tuesday, June 23, Fortraโ€™s Jerrod Foster & Michael Barford will joinย The New Stackย to discuss IT and OT systems, why extracting operational technology data is challenging, and how Fortra GoAnywhere MFT can resolve both data movement and data security issues that many engineers face today.

Register here to join the conversation:

What youโ€™ll take away:

  • Why the IT/OT boundary is an AI infrastructure problem: How the connectivity gap between operational and information technology creates a hard ceiling for teams building on live operational data โ€” and what becomes possible when that data is reliably accessible inside modern pipelines.
  • Where DIY solutions break: Why custom scripts and legacy transfer tools fail under real operational conditions โ€” brittle transfers, no visibility, and attack surfaces you canโ€™t audit.
  • What secure OT data movement actually looks like: How Fortra GoAnywhere MFT provides an encrypted, automated, and auditable data movement layer that works with the constraints of real OT environments, not against them.

The post How to get operational data off the factory floor without creating an IT breach appeared first on The New Stack.

Build Personal AI Agents on Windows PCs with New Tools from Microsoft and NVIDIA

AI agents are changing how you interact with your PC. Creators, developers, and AI enthusiasts are already using these agents extensively to assist with...

AI agents are changing how you interact with your PC. Creators, developers, and AI enthusiasts are already using these agents extensively to assist with day-to-day tasks such as coding, video editing, and content management. NVIDIA and Microsoft are teaming up to enable the next generation of developers to build on-device agents on the Windows platform, with easier setup, native securityโ€ฆ

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Deploy Agentic-Ready AI at the Edge with Memory Efficiency in NVIDIA JetPack 7.2

2 June 2026 at 02:00
As AI agents move from the digital world to the physical environment, they can readily use NVIDIA Jetson to accelerate real-world deployment with optimized...

As AI agents move from the digital world to the physical environment, they can readily use NVIDIA Jetson to accelerate real-world deployment with optimized memory and performance. NVIDIA JetPack 7.2 directly supports one-command deployment of NVIDIA NemoClaw, an open source stack that adds privacy and security controls to OpenClaw. It introduces NVIDIA agent skills for Jetsonโ€”Jetson deviceโ€ฆ

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Accelerated X-Ray Analysis for Nanoscale Imaging (XANI) of Novel Materials

13 May 2026 at 16:39
A massive-scale X-ray free-electron laser (XFEL) enables tracking structural and electron dynamics in novel systems, including fusion materials, semiconductors,...

A massive-scale X-ray free-electron laser (XFEL) enables tracking structural and electron dynamics in novel systems, including fusion materials, semiconductors, batteries, and catalysis. It produces ultrashort X-ray pulses that can record the movements of atoms and electrons. These instruments can detect the smallest change in material structure caused by defects and other influences.

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Model Quantization: Post-Training Quantization Using NVIDIA Model Optimizer

7 May 2026 at 21:18
This post is the second of a three-part series. See also Model Quantization: Concepts, Methods, and Why It Matters and Model Quantization: Turn FP8 Checkpoints...

This post is the second of a three-part series. See also Model Quantization: Concepts, Methods, and Why It Matters and Model Quantization: Turn FP8 Checkpoints into High-Performance Inference Engines with NVIDIA TensorRT. Model quantization is an effective method to reduce VRAM usage and improve inference performance on consumer devices such as NVIDIA GeForce RTX GPUs.

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How to Build In-Vehicle AI Agents with NVIDIA: From Cloud to Carย 

5 May 2026 at 16:00
The automotive cockpit is undergoing a fundamental shift from rule-based interfaces to agentic, multimodal AI systems capable of reasoning, planning, and...

The automotive cockpit is undergoing a fundamental shift from rule-based interfaces to agentic, multimodal AI systems capable of reasoning, planning, and acting. In most vehicles on the road today, in-vehicle assistants still rely on fixed command-response patterns: interpret a phrase, trigger an action, reset. While effective for well-defined tasks, this approach doesnโ€™t scale to modernโ€ฆ

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Scaling Biomolecular Modeling Using Context Parallelism in NVIDIA BioNeMo

28 April 2026 at 19:00
For decades, computational biology has operated under a reductionist compromise. To fit complex biological systems into the limited memory of a single GPU,...

For decades, computational biology has operated under a reductionist compromise. To fit complex biological systems into the limited memory of a single GPU, researchers have had to deconstruct them into isolated fragmentsโ€”single proteins or small domains. This created a context gap, where larger proteins or complexes could not be folded zero-shot due to GPU hardware memory constraints. Nowโ€ฆ

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Federated Learning Without the Refactoring Overhead Using NVIDIA FLARE

24 April 2026 at 15:00
Connected healthcare facilities graphicFederated learning (FL) is no longer a research curiosityโ€”itโ€™s a practical response to a hard constraint: the most valuable data is often the least movable....Connected healthcare facilities graphic

Federated learning (FL) is no longer a research curiosityโ€”itโ€™s a practical response to a hard constraint: the most valuable data is often the least movable. Regulatory boundaries, data sovereignty rules, and organizational risk tolerance routinely prevent centralized aggregation. Meanwhile, sheer data gravity makes even permitted transfers slow, expensive, and fragile at scale.

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Maximizing Memory Efficiency to Run Bigger Models on NVIDIA Jetson

20 April 2026 at 23:01
Decorative image.The boom in open source generative AI models is pushing beyond data centers into machines operating in the physical world. Developers are eager to deploy these...Decorative image.

The boom in open source generative AI models is pushing beyond data centers into machines operating in the physical world. Developers are eager to deploy these models at the edge, enabling physical AI agents and autonomous robots to automate heavy-duty tasks. A key challenge is efficiently running multi-billion-parameter models on edge devices with limited memory. With ongoing constraints onโ€ฆ

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