Besfore you implement NaaS security controls, it’s important that you conduct a detailed technical assessment of your current and target architectures. Comparing the core architectural differences for security across traditional and NaaS environments.
Besfore you implement NaaS security controls, it’s important that you conduct a detailed technical assessment of your current and target architectures.
Comparing the core architectural differences for security across traditional and NaaS environments.
Stay ahead with more perspectives on cutting-edge power, infrastructure, energy, bitcoin and AI solutions. Explore these articles to uncover strategies and insights shaping the future of industries.
Besfore you implement NaaS security controls, it’s important that you conduct a detailed technical assessment of your current and target architectures. Comparing the core architectural differences for security across traditional and NaaS environments.
The reversal marks a dramatic shift from the aggressive stance the Trump administration took in May, when it imposed sweeping restrictions on electronic design automation (EDA) software — the critical tools needed to design advanced semiconductors. A short-lived stoppage The restrictions had targeted what analysts called the “upstream” of chip
The affected products-Cisco Unified CM and Unified CM SME–are core components of enterprise telephony infrastructure, widely deployed across government agencies, financial institutions, and large corporations to manage voice, video, and messaging at scale. A flaw in these systems could allow attackers to compromise an organization’s communications, letting them log in
“We have agreed with the DOJ to offer a license, through an auction, to specific aspects of Juniper Mist, which is just the AI operations part,” HPE CEO Antonio Neri explained during the press conference. The distinction is technically significant. Competitors will gain access to Mist’s anomaly detection and predictive failure
Load growth, plant retirements could drive 100x increase in blackouts by 2030: DOE | Utility Dive Skip to main content An article from Dive Brief The U.S. Department of Energy on Monday published a methodology for assessing grid reliability, but clean energy advocates say it likely exaggerates the risks of blackouts. Published July 8, 2025 Power outage hours could increase from single digits today “to more than 800 hours per year,” the U.S. Department of Energy said in a report published July 7, 2025. Marizza via Getty Images Dive Brief: Blackouts could increase by 100 times in 2030, relative to today’s averages, if the United States continues to shutter power plants and fails to add additional firm capacity amid rising demand, the U.S. Department of Energy said in a Monday report. The report includes a uniform methodology to identify regions at risk of power outages and guide federal reliability “interventions,” DOE said. The report was required by President Donald Trump’s April executive order which directed the agency to respond to an “energy emergency” he declared in January. But clean energy advocates say the report appears to exaggerate the risks, and undercount the contributions of wind, solar and battery storage resources. “If the analysis is overly pessimistic about advanced energy technologies and the future of the grid, consumers will end up paying too much for resources we no longer need,” Caitlin Marquis, managing director at Advanced Energy United, said in an email. Dive Insight: DOE’s report assumes 104 GW of plant retirements by 2030, alongside the addition of 210 GW of new generation — but only 22 GW of the additions will be “firm, reliable, dispatchable generation.” “Modeling shows annual outage hours could increase from single digits today to more than 800 hours per year. Such a surge would leave millions of households and
The PJM Interconnection’s market monitor on Monday urged federal regulators to set conditions on a pending $12 billion deal between NRG Energy and LS Power citing concern over potential market abuses. Under the planned transaction, NRG would buy from LS Power 18 natural gas-fired power plants, totaling about 13 GW across nine states, some of which fall in PJM’s territory. NRG would also acquire CPower, a commercial and industrial virtual power plant platform that has about 6 GW of capacity and operates in all deregulated markets in the U.S., according to a news release announcing the deal. Setting conditions on the deal would help prevent NRG from exerting market power, Monitoring Analytics, PJM’s independent market monitor, said in a filing at the Federal Energy Regulatory Commission. The companies expect to close the deal early next year, subject to FERC and other approvals. Without the conditions, Monitoring Analytics said it would oppose the transaction because it would increase NRG’s ability to affect electricity and capacity prices. Under the pending deal, NRG’s capacity in PJM would jump from about 2.1 GW to 9.5 GW. The transaction would also add to NRG’s emergency and pre-emergency demand response capability. Even with the increased capacity, NRG and LS Power contend that NRG would be unable to exert market power, according to their June 12 application at FERC. However, the transaction would increase NRG’s market power in some parts of PJM, according to Monitoring Analytics. Also, under PJM’s market conditions, capacity prices are likely to continue being “very high,” the market monitor said. “The market conditions and structural market power increase the potential impact of market power on customers under the current market structure,” it said. The market monitor said it is concerned that NRG would be able to affect capacity prices using its increased ownership in emergency and
United Energy LNG and Power LNG signed Monday a merger agreement to form “a scalable small-scale LNG platform serving industrial, power, and transport markets across North America”. United Energy LNG will survive as the expanded entity, immediately overseeing three liquefied natural gas (LNG) production sites in “advanced stages of development”, a joint statement said. The projects have an estimated capital of $240-270 million. Concurrently United Energy LNG announced the start of the first phase of front-end loading (FEL-1), or early planning work, for the first project site, in Houston, Texas. It said the second project is “underway” in a confidential location while the third site, in Independence, Kansas, is scheduled to begin FEL-1 this month. United Energy LNG plans to achieve financial close on the projects by the first quarter of 2026. It expects to commission the projects by the first quarter of 2027. “The company is actively engaging with feedgas suppliers, utilities, and logistics providers to build out a nationwide virtual LNG delivery network”, the statement said. “The company estimates that its addressable domestic market exceeds 2 billion cubic feet equivalent annually, representing a $3+ billion opportunity in underserved power, transport, and industrial sectors”. The combined entity targets a liquefaction capacity of “up to 540,000 MTPA at full deployment”, the companies said. “While the majors are focused on megaprojects, we’re deploying modular, nimble solutions that get LNG to the people and industries who need it now”, said United Energy LNG chief executive Brian Guinn. Power LNG chief executive Austin Terry said, “This merger represents the alignment of speed, innovation, and execution. By combining our development and operational expertise with United Energy’s capital platform, we’re unlocking a scalable model for delivering LNG where it’s needed most”. The companies said, “Unlike traditional LNG developments requiring multi-billion-dollar investments and long permitting timelines,
Natural gas and water utility NW Natural Holding Co. (NW Natural Holdings) and its subsidiary Northwest Natural Gas Co. (NW Natural) have appointed the chief of a construction firm and a sportswear executive as directors. Peter Bragdon, executive vice president, chief administrative officer and general counsel of Columbia Sportswear Co., will serve as an independent director at Portland, Oregon-based NW Natural Holdings effective July 12. NW Natural also named Bragdon to its board of directors from the same date, as well as Hoffman Construction Co. president and chief executive Dave Drinkward effective June 30. “They are both leaders at respected, industry-leading companies and their insights will be invaluable as the company continues to grow”, NW Natural Holdings board chair Malia H. Wasson said in an online statement. “We greatly value both Peter and Dave’s contributions to our local Pacific Northwest community, which is in line with NW Natural’s core values”. Bragdon has served in his current roles at Columbia since 2015. Previously he served as chief of staff at Oregon’s gubernatorial office from January 2003 to June 2004. Currently Bragdon serves as board chair at the Oregon Community Foundation. He also serves on the boards of the World Federation of the Sporting Goods Industry and the Footwear Distributors and Retailers of America, as well as the board of trustees of Reed College. Bragdon received a Juris Doctorate from Stanford Law School, a Master of Studies in Law degree from Yale Law School and a Bachelor of Arts (BA) degree from Amherst College, according to NW Natural Holdings. Drinkward has held his current positions at Hoffman since November 2018. Drinkward also serves on the boards of several organizations including the ACE Mentor Program of America, Meals on Wheels People and the Oregon Business Council. He is also a trustee of Willamette
It appears that Iran was not very close to trying to block shipping through the Strait of Hormuz. That’s what Edward L. Morse, Senior Advisor and Commodities Strategist at Hartree Partners LP, and previously the Global Head of Commodity Research at Citi Group, told Rigzone in an exclusive interview recently. “Iran’s position in the Middle East and in the global market has deteriorated significantly since the October 7 Hamas attacks in Southern Israel, which Iran with no question helped to orchestrate,” Morse said. “It has lost effective utility of all of its external proxies. It has lost its position in a number of countries almost completely … It has lost its air defense completely. And it … [has] seen its vulnerability to its control over the domestic economy, including the availability of natural gas for power generation and industry as summer demand was starting to surge,” he added. “On top of that the combination of Israeli and particularly U.S. attacks have reduced the possibility of significant assistance from the two outside powers tha[t]… have been their main global backers – Russia and China,” he continued. “The former has limited ability to provide arms given its situation vis-à-vis Ukraine while the later has absolutely no interest in seeing a disruption of critical supplies through the Strait of Hormuz,” Morse went on to state. “In short, a shutting of flows through the Strait would actually be an existential threat to Iran, and a threat to do that is not only not a critical element of its national security, it is a direct danger to its existence,” he highlighted. When asked how high oil prices would have climbed if Iran had shut the Strait, Morse told Rigzone that it is close to impossible to see how much oil prices would have increased had Iran shut
DNO ASA said its Norwegian operating subsidiaries have entered into an offtake agreement with France’s ENGIE SA for the company’s Norwegian gas production. The offtake agreement covers the entirety of DNO’s Norwegian gas production following its acquisition of Sval Energi Group AS, the company said in a news release. The four-year agreement is effective October 1 and “offers premium pricing,” DNO said. Related to the agreement, DNO said it has entered into an offtake financing facility with an undisclosed U.S. bank for up to $500 million. Under the facility, the bank finances DNO the value of up to 270 days of scheduled gas production based on future gas sales receivables. The all-in interest rate for drawn amounts under the facility is “significantly below” conventional reserve-based lending (RBL) terms available to DNO, with no charges for undrawn amounts, DNO said, adding that there are no financial covenants related to the facility. Proceeds from the offtake financing facility will be used to replace Sval Energi’s existing similar facilities as well as for general corporate purposes, DNO stated. “We have received strong interest by buyers to prepurchase our enlarged North Sea production of 80,000 barrels of oil equivalent per day split about equally between oil and gas,” DNO Executive Chairman Bijan Mossavar-Rahmani said. “These three-way transactions are made possible because buyers are eager to lock in secure supplies of Norwegian oil and gas and U.S. banks, in particular, have significantly stepped up fossil fuel lending,” he added. DNO said it has repaid and will not renew over $ 600 million in RBLs across its North Sea subsidiaries, “given [the] availability of attractive offtake financing terms”. In addition, the company has borrowed $300 million under a one-year bank bridge loan “to add more arrows to our quiver,” Mossavar-Rahmani said. Separately, DNO is also in
Such a shift, analysts say, could offer short-term benefits for enterprises, particularly in cost and access, but also introduces new operational risks. “This acquisition may potentially lower enterprise pricing through lease cost elimination and annual savings, while improving GPU access via expanded power capacity, enabling faster deployment of Nvidia chipsets and systems,” said Charlie Dai, VP and principal analyst at Forrester. “However, service reliability risks persist during this crypto-to-AI retrofitting.” This also indicates that struggling vendors such as Core Scientific and similar have a way to cash out, according to Yugal Joshi, partner at Everest Group. “However, it does not materially impact the availability of Nvidia GPUs and similar for enterprises,” Joshi added. “Consolidation does impact the pricing power of vendors.” Concerns for enterprises Rising demand for AI-ready infrastructure can raise concerns among enterprises, particularly over access to power-rich data centers and future capacity constraints. “The biggest concern that CIOs should have with this acquisition is that mature data center infrastructure with dedicated power is an acquisition target,” said Hyoun Park, CEO and chief analyst at Amalgam Insights. “This may turn out to create challenges for CIOs currently collocating data workloads or seeking to keep more of their data loads on private data centers rather than in the cloud.”
The deployment, Kimball said, “brings Dell quality to the commodity space. Wins like this really validate what Dell has been doing in reshaping its portfolio to accommodate the needs of the market — both in the cloud and the enterprise.” Although concerns were voiced last year that Nvidia’s next-generation Blackwell data center processors had significant overheating problems when they were installed in high-capacity server racks, he said that a repeat performance is unlikely. Nvidia, said Kimball “has been very disciplined in its approach with its GPUs and not shipping silicon until it is ready. And Dell almost doubles down on this maniacal quality focus. I don’t mean to sound like I have blind faith, but I’ve watched both companies over the last several years be intentional in delivering product in volume. Especially as the competitive market starts to shape up more strongly, I expect there is an extremely high degree of confidence in quality.” CoreWeave ‘has one purpose’ He said, “like Lambda Labs, Crusoe and others, [CoreWeave] seemingly has one purpose (for now): deliver GPU capacity to the market. While I expect these cloud providers will expand in services, I think for now the type of customer employing services is on the early adopter side of AI. From an enterprise perspective, I have to think that organizations well into their AI journey are the consumers of CoreWeave.” “CoreWeave is also being utilized by a lot of the model providers and tech vendors playing in the AI space,” Kimball pointed out. “For instance, it’s public knowledge that Microsoft, OpenAI, Meta, IBM and others use CoreWeave GPUs for model training and more. It makes sense. These are the customers that truly benefit from the performance lift that we see from generation to generation.”
“For CIOs, this shift means more competition for AI infrastructure. Over the next 12–24 months, securing capacity for AI workloads will likely get harder, not easier. Though cost is coming down but demand is increasing as well, due to which CIOs must plan earlier and build stronger partnerships to ensure availability,” said Pareekh Jain, CEO at EIIRTrend & Pareekh Consulting. He added that CIOs should expect longer wait times for AI infrastructure. To mitigate this, they should lock in capacity through reserved instances, diversify across regions and cloud providers, and work with vendors to align on long-term demand forecasts. “Enterprises stand to benefit from more efficient and cost-effective AI infrastructure tailored to specialized AI workloads, significantly lower their overall future AI-related investments and expenses. Consequently, CIOs face a critical task: to analyze and predict the diverse AI workloads that will prevail across their organizations, business units, functions, and employee personas in the future. This foresight will be crucial in prioritizing and optimizing AI workloads for either in-house deployment or outsourced infrastructure, ensuring strategic and efficient resource allocation,” said Neil Shah, vice president at Counterpoint Research. Strategic pivot toward AI data centers The OpenAI-Oracle deal comes in stark contrast to developments earlier this year. In April, AWS was reported to be scaling back its plans for leasing new colocation capacity — a move that AWS Vice President for global data centers Kevin Miller described as routine capacity management, not a shift in long-term expansion plans. Still, these announcements raised questions around whether the hyperscale data center boom was beginning to plateau. “This isn’t a slowdown, it’s a strategic pivot. The era of building generic data center capacity is over. The new global imperative is a race for specialized, high-density, AI-ready compute. Hyperscalers are not slowing down; they are reallocating their capital to
What this doesn’t answer is how Arista Networks plans to add newer, security-oriented Secure Access Service Edge (SASE) capabilities to VeloCloud’s older SD-WAN technology. Post-acquisition, it still has only some of the building blocks necessary to achieve this. Mapping AI However, in 2025 there is always more going on with networking acquisitions than simply adding another brick to the wall, and in this case it’s the way AI is changing data flows across networks. “In the new AI era, the concepts of what comprises a user and a site in a WAN have changed fundamentally. The introduction of agentic AI even changes what might be considered a user,” wrote Arista Networks CEO, Jayshree Ullal, in a blog highlighting AI’s effect on WAN architectures. “In addition to people accessing data on demand, new AI agents will be deployed to access data independently, adapting over time to solve problems and enhance user productivity,” she said. Specifically, WANs needed modernization to cope with the effect AI traffic flows are having on data center traffic. Sanjay Uppal, now VP and general manager of the new VeloCloud Division at Arista Networks, elaborated. “The next step in SD-WAN is to identify, secure and optimize agentic AI traffic across that distributed enterprise, this time from all end points across to branches, campus sites, and the different data center locations, both public and private,” he wrote. “The best way to grab this opportunity was in partnership with a networking systems leader, as customers were increasingly looking for a comprehensive solution from LAN/Campus across the WAN to the data center.”
However, even though colocation and on-premises data centers will continue to lose share, they will still continue to grow. They just won’t be growing as fast as hyperscalers. So, it creates the illusion of shrinkage when it’s actually just slower growth. In fact, after a sustained period of essentially no growth, on-premises data center capacity is receiving a boost thanks to genAI applications and GPU infrastructure. “While most enterprise workloads are gravitating towards cloud providers or to off-premise colo facilities, a substantial subset are staying on-premise, driving a substantial increase in enterprise GPU servers,” said John Dinsdale, a chief analyst at Synergy Research Group.
He pointed out that, in addition to its continued growth, OCI has a remaining performance obligation (RPO) — total future revenue expected from contracts not yet reported as revenue — of $138 billion, a 41% increase, year over year. The company is benefiting from the immense demand for cloud computing largely driven by AI models. While traditionally an enterprise resource planning (ERP) company, Oracle launched OCI in 2016 and has been strategically investing in AI and data center infrastructure that can support gigawatts of capacity. Notably, it is a partner in the $500 billion SoftBank-backed Stargate project, along with OpenAI, Arm, Microsoft, and Nvidia, that will build out data center infrastructure in the US. Along with that, the company is reportedly spending about $40 billion on Nvidia chips for a massive new data center in Abilene, Texas, that will serve as Stargate’s first location in the country. Further, the company has signaled its plans to significantly increase its investment in Abu Dhabi to grow out its cloud and AI offerings in the UAE; has partnered with IBM to advance agentic AI; has launched more than 50 genAI use cases with Cohere; and is a key provider for ByteDance, which has said it plans to invest $20 billion in global cloud infrastructure this year, notably in Johor, Malaysia. Ellison’s plan: dominate the cloud world CTO and co-founder Larry Ellison announced in a recent earnings call Oracle’s intent to become No. 1 in cloud databases, cloud applications, and the construction and operation of cloud data centers. He said Oracle is uniquely positioned because it has so much enterprise data stored in its databases. He also highlighted the company’s flexible multi-cloud strategy and said that the latest version of its database, Oracle 23ai, is specifically tailored to the needs of AI workloads. Oracle
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.
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
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
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
In partnership withPlaid From a cluster of call centers in Canada, a criminal network defrauded elderly victims in the US out of $21 million in
In partnership withMichigan Economic Development Corporation Michigan may be best known as the birthplace of the American auto industry, but its innovation legacy runs far
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Today’s AI landscape is defined by the ways in which neural networks are unlike human brains. A toddler learns how to communicate effectively with only
