Cato extends SASE platform to unmanaged devices

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Cato extends SASE platform to unmanaged devices

Cato Networks this week introduced the Cato Browser Extension that expands the company’s secure access service edge (SASE) platform and its universal zero-trust network access (ZTNA) capabilities to unmanaged devices and distributed bring-your-own-device (BYOD) endpoints. The Cato Browser Extension, according to the company, is a lightweight onramp to its SASE platform, which brings zero-trust policies […]

The Cato Browser Extension, according to the company, is a lightweight onramp to its SASE platform, which brings zero-trust policies and enforcement to unmanaged and other devices, such as personal, partner, contractor, or other endpoints, without requiring clients to install new software.

“Unmanaged and BYOD devices aren’t going away. They remain the weakest links in enterprise security. Without the safeguards of managed devices, they expose organizations to additional risk,” said Ofir Agasi, chief product officer at Cato Networks, in a statement. “The Cato Browser Extension provides customers with the best of both worlds: simple access for users and complete policy control for IT without adding another tool to manage.”

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IBM, Red Hat, Palo Alto team to secure open-source software

“The clearinghouse will serve as a security coordination layer, using advanced AI capabilities to validate and test fixes across an unprecedented volume of open source code,” IBM stated in May. “These capabilities will be offered through commercial subscriptions, allowing enterprises to integrate secure patches directly into their existing software supply

Attackers exploit Cisco Unified CM flaw weeks after patch release

The flaw is tracked as CVE-2026-20230 and carries a CVSS base score of 8.6. Cisco published the advisory and patches on June 3, when it stated it was not aware of any malicious use of the vulnerability at the time of disclosure. “This vulnerability is due to improper input validation

Rami Rahim’s message for network pros: Legacy networks can’t withstand rigors of AI

Architect for AI-era baselines, not legacy traffic assumptions. Design for sustained east-west flows, low jitter, and deterministic paths that support AI training and inference, not just classic north-south traffic. Translate network health into AI business outcomes. When you speak to leadership, link latency, loss, and path diversity to model training

China’s LineShine dethrones El Capitan as the world’s fastest supercomputer

The latest rankings also mean there are now five exascale systems operating globally, with LineShine joining El Capitan, Frontier, Aurora, and Europe’s JUPITER Booster in an increasingly crowded upper tier. While the U.S. systems dominating the upper echelon rely heavily on accelerators from AMD and Intel, LineShine reached the top

Department of Energy Announces American Nuclear Supply Chain Loans

WASHINGTON—The U.S. Department of Energy’s (DOE) Office of Energy Dominance Financing (EDF) issued a conditional loan commitment to finance the purchase of long-lead time items needed to rebuild America’s commercial nuclear supply chain. The $17.5 billion American Nuclear Supply Chain Loans will help finance five eligible projects sponsored by utilities and energy companies nationwide to accelerate the deployment of 10 large-scale commercial nuclear reactors across the United States by up to three years. The project marks a major step toward advancing President Trump’s Executive Order, Reinvigorating the Nuclear Industrial Base, by supporting the objective of having 10 new large nuclear reactors with complete designs under construction by 2030. “Just over one year ago, President Trump directed the Energy Department and its agency partners to unleash the next American nuclear renaissance,” U.S. Energy Secretary Chris Wright said. “To accomplish that mission, these conditional loans will play an important role in reviving the supply chain needed for America to once again build large-scale commercial reactors. They will also help accelerate the timeline of building those large-scale reactors by up to three years, lowering construction costs and ensuring the United States is able to deliver on President Trump’s bold and ambitious energy addition agenda.” Westinghouse’s AP1000® units are the only licensed large-scale advanced commercial reactors operating in the United States today. Long-lead items are complex components of a nuclear power plant that require the longest time for manufacturing and delivery. EDF financing will support up to five loans, each loan supporting two reactors at a project site. Westinghouse will partner with up to five eligible utilities and energy companies nationwide to procure the long-lead items at a fixed price. Each project will be jointly owned by Westinghouse and a utility or energy company partner. Both Westinghouse and the partner are required to

FPSO ready for Santos-led Barossa LNG project

BW Offshore completed the Interim Performance Test (IPT) for the BW Opal floating production, storage, and offloading vessel (FPSO) as part of the commissioning program for the Santos Ltd.-operated Barossa LNG project about 285 km offshore from Darwin in the Northern Territory of Australia. The milestone is part of early-stage technical testing and adjustments following first gas from the FPSO in September and the beginning of flow from subsea wells. BW Offshore confirmed that key production, processing, and utility systems on the FPSO were operating in an integrated manner and capable of delivering stable performance under production conditions. Following the restart of production in early May, BW Opal has continued gas production and export. Production is being managed in close coordination with Santos during this phase of the ramp-up and commissioning program. BW Opal contains a 358-m hull and accommodation for up to 140 personnel. It has gas handling capacity of 850 MMscfd and condensate handling capacity of 11,000 b/d. The FPSO will feed the Darwin LNG plant for the next two decades. The Barossa LNG project consists of the FPSO, a subsea production system, supporting in-field subsea infrastructure, a gas export pipeline, and a Darwin pipeline duplication. Up to eight subsea wells are planned (six wells from three drill centers) with contingency plans for an additional two wells. Gas and condensate is gathered from the wells through the subsea production system and then brought to the FPSO via a network of subsea infrastructure. Santos operates the Barossa LNG project (50%) with joint venture partners PRISM Energy International Australia Pty Ltd. (37.5%) and JERA Australia (12.5%).

Equinor mulls additional Johan Sverdrup development phase

Equinor Energy AS is considering further development of the Johan Sverdrup area resources in the North Sea. Production from discoveries in Tonjer west and east and Geitungen would form the basis for the maturation of a potential phase 4 development in the northern part of the field. The volumes would be developed via subsea tieback to existing Johan Sverdrup infrastructure. Tonjer lies in the northernmost part of the Geitungen terrace in the Johan Sverdrup area. Oil was discovered in the area, but volumes and potential have been uncertain. The drilling of two appraisal wells and a sidetrack have provided a more precise assessment of the resource base. Preliminary estimates for Tonjer and Geitungen combined are 20-30 MMboe. Further analyses of subsurface data will form the basis for more precise resource estimates. Phase 4 is now being matured towards an investment decision with a possible production start-up in 2029. Johan Sverdrup Johan Sverdrup, which accounts for about one third of Norwegian oil production, lies on the Utsira High (Utsirahøyden) in the central part of the North Sea, 65 km northeast of Sleipner field in water depths of 115 m. The main reservoir contains oil in Upper Jurassic intra-Draupne sandstone. The reservoir depth is 1,900 m. The quality of the main reservoir is excellent with very high permeability. The remaining oil resources are in sandstone in the Upper Triassic Statfjord Group and Middle to Upper Jurassic Vestland Group, as well as in spiculites in the Upper Jurassic Viking Group. Oil was also proven in Permian Zechstein carbonates. Equinor is operator of Johan Sverdrup (42.62%) with partners Aker BP (31.57%), Petoro (17.36%), and TotalEnergies (8.44%).

Beacon advances deepwater Gulf developments with Monument, Zephyrus field work

Beacon Offshore Energy LLC is advancing two deepwater Gulf of Mexico developments, having drilled the first development well at Monument field and brought a second production well online at Zephyrus field. At Monument in Walker Ridge Block 315, the first development well reached a total depth of 32,250 ft and encountered 245 ft of net pay (true vertical thickness) in Lower Wilcox reservoirs, confirming pre-drill expectations for reservoir quality, the operator said. Beacon will continue drilling a second development well before completing the initial two-well program. First oil from the Wilcox development is expected before yearend 2026. Monument is being developed through a two-well, 17-mile subsea tieback to the Beacon-operated Shenandoah floating production system, which was designed as a regional host platform for developments in the northwestern Walker Ridge area, including Shenandoah, Monument, and Shenandoah South fields. Partners are Navitas Petroleum and Talos Energy Inc. At Zephyrus in Mississippi Canyon Block 759, production from the Zephyrus #2 well began in late April after the well was completed in first-quarter 2026. The well is producing from Miocene sands. Combined with Zephyrus #1, which started production in late 2025, the field is expected to reach peak production of more than 20,000 boe/d. The Zephyrus development is tied back to the Shell plc-operated West Boreas subsea infrastructure, with production processed on the Olympus tension-leg platform in the Mars corridor. Partners are Houston Energy, HEQ II, Red Willow Offshore, Westlawn Americas Offshore, and Murphy Exploration & Production.

Greece approves Chevron’s farm-in for offshore Block 10

Greece approved Chevron Corp.’s farm-in to offshore Block 10, clearing the way for the US major to complete its acquisition of a 70% interest and operatorship from HELLENiQ Energy. Greece’s Ministry of Environment and Energy and the Hellenic Hydrocarbon and Energy Resources Management Co. (HHRE) said June 15 that all administrative approvals have been completed for the transfer of the interest and operatorship. Chevron and HELLENiQ submitted the request for approval May 28. The companies also requested a 15-month extension of the second exploration phase for the block, which lies offshore the Kyparissia Gulf in the southern Ionian Sea. Following completion of the transfer, Chevron will hold a 70% interest and serve as operator, while HELLENiQ will retain the remaining 30%. Geological, geophysical, and environmental studies have been completed on the concession, including acquisition of 1,210 km of 2D seismic data in 2022 followed by 2,416 sq km of 3D seismic covering 88% of the block. The partners will use the seismic data to evaluate potential drilling targets before deciding whether to proceed to a third exploration phase, which includes an exploratory well. Chevron and HELLENiQ are already partners in four offshore concessions south of Crete and the Peloponnese, making Block 10 their fifth joint offshore license in Greece. Chevron said the agreement advances its strategy of expanding its exploration portfolio in the Eastern Mediterranean. Greek officials said the investment reflects confidence in the country’s offshore licensing framework and supports its long-term goal of strengthening Greece’s role in regional energy supply if exploration proves successful.

Comstock farms out minority interest in midstream subsidiary for $600 million

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KKR Bets Big on AI Infrastructure With Helix Launch, Tapping Former AWS CEO Adam Selipsky to Build a New Hyperscale Model

To close industry watchers, it’s really no secret that the AI infrastructure race has entered another phase; one where capital formation itself may become as strategically important as GPUs, power procurement, or liquid cooling. And in launching Helix Digital Infrastructure, investment giant KKR is making a calculated wager that hyperscalers no longer simply need developers or financiers. They need a partner capable of orchestrating capital, energy, connectivity, and data center execution as a unified platform. The significance of that strategy is underscored by the executive chosen to lead it. Adam Selipsky, the former CEO of Amazon Web Services and one of the industry’s most experienced cloud operators, will serve as Co-Founder and CEO of Helix, bringing firsthand experience from the very class of customers the new venture intends to serve. A New Model for AI Infrastructure Helix launches with more than $10 billion in long-duration committed capital from founding investors including KKR, the Kuwait Investment Authority (KIA), NVIDIA, and Vistra. But the headline number tells only part of the story. The company has been structured around an increasingly important thesis: that AI infrastructure can no longer be assembled piecemeal. Rather than treating data centers, electrical supply, transmission capacity, and fiber connectivity as separate procurement exercises, Helix proposes a vertically coordinated approach in which a single organization manages and finances the entire infrastructure stack. According to KKR, the objective is to reduce execution risk and accelerate deployment for hyperscale customers facing unprecedented AI demand. As AI factories grow from hundreds of megawatts toward gigawatt-scale campuses, synchronization among land acquisition, utility planning, financing, construction, and technology deployment has emerged as one of the industry’s defining challenges. Helix is effectively positioning itself as an operating platform designed to simplify that complexity. Why Selipsky Matters The appointment of Adam Selipsky may be the announcement’s

Beyond Hyperscale: Why Enterprise Data Centers Still Matter in the AI Era

“The enterprise data centers, even the new ones, tend to be far, far smaller than new hyperscale deployments,” Killian said. “Not uncommon to see enterprises deploy a quarter meg or one meg or two, maybe up to 10 megs. Whereas the hyperscale guys are deploying 40 up to 300 meg facilities.” But scale alone does not tell the story. For every one of the roughly 20 hyperscale users that dominate headlines, Killian noted, there may be 50 to 100 times as many large and mid-sized enterprise users. Those companies run critical business systems, purchase hardware, software, telecom and services, employ large data center teams, and often operate multiple facilities across domestic, edge, EMEA and Asia-Pacific footprints. In other words, enterprise demand may be smaller in unit size, but it remains massive in aggregate. And as AI shifts from training to inference, the enterprise data center could become newly strategic. Enterprise AI Is Not Hyperscale AI Killian’s central point is that enterprise infrastructure requirements differ materially from hyperscale requirements. Hyperscalers are primarily optimizing for massive scale and speed to market. Enterprises, by contrast, tend to prioritize reliability, flexibility, integration into broader IT systems, and audit and compliance. That difference has major implications for developers and colocation providers. “The real industry opportunity is to take some of the innovation and the economies of scale that we’re seeing from the hyperscale builds to deliver smaller chunks of data center capacity,” Killian said. That might mean adapting lessons from 40 MW or 100 MW campuses into enterprise-ready deployments of 2 MW, 4 MW or 8 MW. Killian pointed to providers such as DataBank and Flexential as examples of companies working to deliver hyperscale-derived efficiencies in smaller enterprise increments. He also noted that QTS and other large campus developers may reserve portions of multi-building campuses

Revolutionizing Data Center Cooling: Innovations for AI and HPC Growth

This is a crucial point for AI infrastructure. In some markets, water can be as politically and operationally difficult as power. Evaporative cooling and cooling towers can consume large volumes of water, while discharge permits can slow projects or limit operations. Gradiant claims HyperSolved can expand access to alternative sources such as municipal reuse and impaired supplies, reduce reliance on freshwater, protect cooling performance through integrated treatment and AI-enabled operations, and minimize discharge through high-recovery concentration and reuse. The platform uses containerized systems for immediate or temporary capacity while also supporting permanent infrastructure and lifecycle operations from commissioning onward. That fits the AI data center buildout, where developers may need bridge capacity during construction, phased water infrastructure, or interim systems while permanent treatment plants are completed. This can address the speed of deployment issue that plagues many data center solutions. Water is becoming a siting and scaling variable that has to be addressed. A site may have land and power prospects, but if water sourcing, reuse, or discharge cannot be solved, the project will face higher costs, delays, and local opposition. Gradiant is positioning itself as the managed water layer for hyperscale AI, similar to how power providers, cooling vendors, and network suppliers each own critical infrastructure domains. The Pattern: Hybridization, Standardization, and Industrial Scale The announcements included here make it clear that cooling is seeing significant attention from technology vendors, and not just state-of-the-art new technologies such as direct-to-chip, but also traditional data center air cooling. T-Global and SiPearl are working on high-conductivity materials and two-phase modules for HPC chips. Castrol is providing fluids for direct-to-chip and immersion environments. These are technologies aimed at the heat source itself, where higher chip power and rack density are overwhelming conventional approaches. The reference design offerings from Johnson Controls acknowledges the importance

Building the AI Factory: Power, Cooling, and Execution at Scale Meets the Deployment Reality Gap – Q2 Executive Roundtable

At Data Center Frontier, we rely on industry leaders not only to help us understand the most urgent challenges reshaping digital infrastructure, but also to illuminate the broader technological, operational, and market forces driving the industry’s evolution. And in the Second Quarter of 2026, those challenges increasingly revolve around a fundamental shift in emphasis: the industry is moving beyond discussing AI infrastructure in theory and into the far more demanding work of deploying, operating, and scaling it in production. The era when hyperscale announcements and GPU roadmaps dominated the conversation is giving way to one defined by execution; where power availability, thermal management, construction schedules, supply chains, and operational discipline determine whether ambitious plans become functioning AI factories. That transition is exposing new realities. Rack densities continue to climb, liquid cooling is becoming mainstream, electrical architectures are evolving, and project timelines are compressing even as capital commitments reach unprecedented levels. Success increasingly depends not on optimizing individual systems in isolation but on orchestrating tightly integrated environments where compute, power, cooling, networking, and facility operations function as a unified whole. At the same time, moving from pilot deployments to industrial-scale AI infrastructure introduces an entirely different class of challenges around reliability, maintainability, commissioning, and repeatable execution. For our Q2 Executive Roundtable, we brought together senior leaders whose expertise spans AI infrastructure design, mission-critical deployment, advanced thermal management, and engineering innovation to examine where the industry stands today, and what it will take to bridge the gap between AI ambition and AI deployment at scale. Drawing on perspectives from hyperscale execution, liquid cooling, and next-generation power and facility engineering, their insights explore the practical realities of building the AI factory at industrial scale.

Upscale AI readies Skyhammer scale-up networking tech, raises new funding

Khemani said that unlike commodity data center chips repurposed for AI, Skyhammer is being developed specifically for AI scale‑up use cases and is tightly coupled to Upscale’s broader full‑stack strategy, which spans silicon, systems and software. Khemani declined to share detailed timelines, but he said Upscale expects to reveal product details on Skyhammer later this year, with actual deployment synced to when GPU and XPU vendors are ready. “The Skyhammer product doesn’t work by itself,” he explained. “It works in conjunction with XPUs and GPUs, and so for us to be deployed, the XPUs and GPUs need to incorporate scale‑up capabilities to interoperate with us.” Nvidia, Spectrum X, and strategic capital Nvidia sits at the center of Upscale AI’s story, both as a technology partner and now as a strategic investor.

Edge networks a particular challenge for summer power, IT staffing needs

Power failures continue to dominate data center outage causes, accounting for 45% of impactful outages in Uptime Institute’s recently released 2026 Annual Outage Analysis report. While that figure declined from the previous year, it remains significantly higher than any other category. Within power-related incidents, UPS failures, transfer switch failures, and generator failures are the leading root causes. Uptime analysts said growing grid instability, power constraints, and high-density compute deployments are creating new pressure points for operators already running closer to capacity limits, according to a recent story on the report in Network World. Beyond power issues, hardware failures—particularly related to storage—also contribute to downtime. He noted that a lack of routine updates, especially to firmware, can make these problems worse, even when the underlying hardware is still functional.

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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