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Why security stacks need to think like an attacker, and score every user in real time

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More More than 40% of corporate fraud is now AI-driven, designed to mimic real users, bypass traditional defenses and scale at speeds that overwhelm even the best-equipped SOCs. In 2024, nearly 90% of enterprises were targeted, and […]

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More than 40% of corporate fraud is now AI-driven, designed to mimic real users, bypass traditional defenses and scale at speeds that overwhelm even the best-equipped SOCs.

In 2024, nearly 90% of enterprises were targeted, and half of them lost $10 million or more.

Bots emulate human behavior and create entire emulation frameworks, synthetic identities, and behavioral spoofing to pull off account takeovers at scale while slipping past legacy firewalls, EDR tools, and siloed fraud detection systems.

Attackers weaponize AI to create bots that evade, mimic, and scale

Attackers aren’t wasting any time capitalizing on using AI to weaponize bots in new ways. Last year, malicious bots comprised 24% of all internet traffic, with 49% classified as ‘advanced bots’ designed to mimic human behavior and execute complex interactions, including account takeovers (ATO).

Over 60% of account takeover (ATO) attempts in 2024 were initiated by bots, capable of breaching a victim’s credentials in real time using emulation frameworks that mimic human behavior. Attacker’s tradecraft now reflects the ability to combine weaponized AI and behavioral attack techniques into a single bot strategy.

That’s proving to be a lethal combination for many enterprises already battling malicious bots whose intrusion attempts often aren’t captured by existing apps and tools in security operations centers (SOCs).

Malicious bot attacks force SOC teams into firefighting mode with little or no warning, depending on the legacy of their security tech stack.

“Once amassed by a threat actor, they can be weaponized,” Ken Dunham, director of the threat research unit at Qualys recently said. “Bots have incredible resources and capabilities to perform anonymous, distributed, asynchronous attacks against targets of choice, such as brute force credential attacks, distributed denial of service attacks, vulnerability scans, attempted exploitation and more.”

From fan frenzy to fraud surface: bots corner the market for Taylor Swift tickets  

Bots are the virtual version of attackers who can scale to millions of attempts per second to attack a targeted enterprise and increasingly high-profile events, including concerts of well-known entertainers, such as Taylor Swift.

Datadome observes that the worldwide popularity of Taylor Swift’s concerts creates the ROI attackers are looking for to build ticket bots that automate what scalpers do at scale. Ticket bots, as Datadome calls them, scoop up massive quantities of tickets at the world’s most popular events and then resell them at significant markups.

The bots flooded Ticketmaster and were a large part of a surge of 3.5 billion requests that hit the ticket site, causing it to crash repeatedly. Thousands of fans were unable to access the presale group, and ultimately, the general ticket sale had to be canceled.

Swarms of weaponized bots froze tens of thousands of Swifties from attending her last Eras concert tour. VentureBeat has learned of comparable attacks on the world’s leading brands on their online stores and presence globally. Dealing with bot attacks at that scale, powered by weaponized AI, is beyond the scope of an e-commerce tech stack to handle – they’re not built to deal with that level of security threat.  

“It’s not just about blocking bots—it’s about restoring fairness,” Benjamin Fabre, CEO of DataDome, told VentureBeat in a recent interview. The company helped See Tickets deflect similar scalping attacks in milliseconds, distinguishing fans from fraud using multi-modal AI and real-time session analysis.

Bot attacks weaponized with AI often start by targeting login and session flows, bypassing endpoints in an attempt not to be detected by standard web application firewalls (WAF) and endpoint detection and response (EDR) tools. Such sophisticated attacks must be tracked and contained in a business’s core security infrastructure, managed from its SOC.

Why SOC teams are now on the front line

Weaponized bots are now a key part of any attacker’s arsenal, capable of scaling beyond what fraud teams alone can contain during an attack. Bots have proven lethal, taking down enterprises’ e-commerce operations or, in the case of Ticketmaster, a best-selling concert tour worth billions in revenue.  

As a result, more enterprises are bolstering the tech stacks supporting their SOCs with online fraud detection (OFD) platforms. Gartner’s Dan Ayoub recently wrote in the firm’s research note Emerging Tech Impact Radar: Online Fraud Detection that “organizations are increasingly waking up to the understanding that ‘fraud is a security problem’ as is becoming evident in adoption of some of the emerging technologies being leveraged today”.

Gartner’s research and VentureBeat’s interviews with CISOs confirm that today’s malicious bot attacks are too fast, stealthy and capable of reconfiguring themselves on the fly for siloed fraud tools to handle. Weaponized bots have long been able to exploit gaps between WAFs, EDR tools and fraud scoring engines, while also evading static rules that are so prevalent in legacy fraud detection systems.

All these factors and more are why CISOs are bringing fraud telemetry into the SOC.

Journey-Time Orchestration is the next wave of online fraud detection (OFD)

AI-enabled bots are constantly learning how to bypass long-standing fraud detection platforms that rely on sporadic or single point-in-time checks. These checks include login validations, transaction scoring tracking over time, and a series of challenge-responses. While these were effective before the widespread weaponization of bots, botnets and networks, AI-literate adversaries now know how to exploit context switching and, as many deepfakes attacks have proven, know how to excel at behavioral mimicry.

Gartner’s research points to Journey Time Orchestration  (JTO) as the defining architecture for the next wave of OFD platforms that will help SOCs better contain the onslaught of AI-driven bot attacks. Core to JTO is embedding fraud defenses throughout each digital session being monitored and scoring risk continuously from login to checkout to post-transaction behavior.

Journey-Time Orchestration continuously scores risk across the entire user session—from login to post-transaction—to detect AI-driven bots. It replaces single-point fraud checks with real-time, session-wide monitoring to counter behavioral mimicry and context-switching attacks. Source: Gartner, Innovation Insight: IAM Journey-Time Orchestration, Feb. 2025

Who’s establishing an early lead in Journey Time Orchestration defense  

DataDome, Ivanti and Telesign are three companies whose approaches show the power of shifting security from static checkpoints to continuous, real-time assessments is paying off. Each also shows why the future of SOCs must be predicated on real-time data to succeed. All three of these companies’ platforms have progressed to delivering scoring for every user interaction down to the API call, delivering greater contextual insight across every behavior on every device, within each session.

What sets these three companies apart is how they’ve taken on the challenges of hardening fraud prevention, automating core security functions while continually improving user experiences. Each combines these strengths on real-time platforms that are also AI-driven and continually learn – two core requirements to keep up with weaponized AI arsenals that include botnets.

DataDome: Thinking Like an Attacker in Real Time

DataDome, A category leader in real-time bot defense, has extensive expertise in AI-intensive behavioral modeling and relies on a platform that includes over 85,000 machine learning models delivered simultaneously across 30+ global PoPs. Their global reach allows them to inspect more than 5 trillion data points daily. Every web, mobile and API request that their platform can identify is scored in real time (typically within 2 milliseconds) using multi-modal AI that correlates device fingerprinting, IP entropy, browser header consistency and behavior biometrics.

“Our philosophy is to think like an attacker,” Fabre told VentureBeat. “That means analyzing every request anew—without assuming trust—and continuously retraining our detection models to adapt to zero-day tactics”​.

Unlike legacy systems, which lean on static heuristics or CAPTCHAs, DataDome’s approach minimizes friction for verified, legitimate users. Its false-positive rate is under 0.01%, meaning fewer than 1 in 10,000 human visitors see a challenge screen. Even when challenged, the platform invisibly continues behavior analysis to verify the user’s legitimacy.

“Bots aren’t just solving CAPTCHAs now—they’re solving them faster than humans,” Fabre added. “That’s why we moved away from static challenges entirely. AI is the only way to beat AI-driven fraud at scale”​.

Case in point: See Tickets used DataDome to defend against the same bot-driven scalping wave that crashed Ticketmaster during the Taylor Swift Eras Tour. DataDome could distinguish bots from fans in milliseconds and prevent bulk buyouts, preserving ticket equity during peak load. In luxury retail, brands like Hermès deploy DataDome to protect high-demand drops (e.g., Birkin bags) from automated hoarding.

Ivanti Extends Zero Trust and exposure management into the SOC

Ivanti is redefining exposure management by integrating real-time fraud signals directly into SOC workflows through its Ivanti Neurons for Zero Trust Access and Ivanti Neurons for Patch Management platforms. “Zero trust doesn’t stop at logins,” Mike Riemer, Ivanti Field CISO told VentureBeat during a recent interview. “We’ve extended it to session behaviors including credential resets, payment submissions, and profile edits are all potential exploit paths.”

Ivanti Neurons continuously evaluates device posture and identity behavior, flagging anomalous activity and enforcing least-privilege access mid-session. “2025 will mark a turning point,” added Daren Goeson, SVP of product management at Ivanti. “Now defenders can use GenAI to correlate behavior across sessions and predict threats faster than any human team ever could.”

As attack surfaces expand, Ivanti’s platform helps SOC teams detect SIM swaps, mitigate lateral movement and automate dynamic microsegmentation. “What we currently call ‘patch management’ should more aptly be named exposure management or how long is your organization willing to be exposed to a specific vulnerability?” Chris Goettl, VP of product management for endpoint security at Ivanti told VentureBeat. “Risk-based algorithms help teams identify high-risk threats amid the noise of numerous updates.”

“Organizations should transition from reactive vulnerability management to a proactive exposure management approach,” added Goeson. “By adopting a continuous approach, they can effectively protect their digital infrastructure from modern cyber risks.”

Telesign’s AI-driven identity intelligence pushes fraud detection to session scale

Telesign is redefining digital trust by bringing identity intelligence at session scale to the front lines of fraud detection. By analyzing more than 2,200 digital identity signals ranging from phone number metadata to device hygiene and IP reputation, Telesign’s APIs deliver real-time risk scores that catch bots and synthetic identities before damage is done.

“AI is the best defense against AI-enabled fraud attacks,” said Telesign CEO Christophe Van de Weyer in a recent interview with VentureBeat. “At Telesign, we are committed to leveraging AI and ML technologies to combat digital fraud, ensuring a more secure and trustworthy digital environment for all.”

Rather than relying on static checkpoints at login or checkout, Telesign’s dynamic risk scoring continuously evaluates behavior throughout the session. “Machine learning has the power to constantly learn how fraudsters behave,” Van de Weyer told VentureBeat. “It can study typical user behaviors to create baselines and build risk models.”

Telesign’s Verify API underscores its omnichannel strategy, enabling identity verification across SMS, email, WhatsApp, and more, all through a single API. “Verifying customers is so important because many kinds of fraud can often be stopped at the ‘front door,’” Van de Weyer noted in a recent VentureBeat interview.

As generative AI accelerates attacker sophistication, Van de Weyer issued a clear call to action: “The emergence of AI has brought the importance of trust in the digital world to the forefront. Businesses that prioritize trust will emerge as leaders in the digital economy.” With AI as its backbone, Telesign looks to turn trust into a competitive advantage.

Why fraud prevention’s future belongs in the SOC

For fraud protection to scale, it must be integrated into the broader security infrastructure stack and owned by the SOC teams who use it to avert potential attacks. Online fraud detection platforms and apps are proving just as critical as APIs, Identity and Access Management (IAM), EDRs, SIEMs and XDRs. VentureBeat is seeing more security teams in SOCs take greater ownership of validating how consumer transactions are modeled, scored and challenged.

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Fluent Bit vulnerabilities could enable full cloud takeover

Attackers could flood monitoring systems with false or misleading events, hide alerts in the noise, or even hijack the telemetry stream entirely, Katz said. The issue is now tracked as CVE-2025-12969 and awaits a severity valuation. Almost equally troubling are other flaws in the “tag” mechanism, which determines how the records are

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Ukraine Says It Hit Saratov Refinery

Ukrainian forces say they have once again attacked Rosneft PJSC’s Saratov oil refinery in Russia’s Volga region as the US pushes for a resolution to the Kremlin’s nearly four-year-old invasion of Ukraine.  “A series of explosions was recorded, followed by a fire in the target area,” Ukraine’s General Staff said in a Telegram statement on Friday, adding that the facility is involved in army supplies. The claim could not be independently verified and Rosneft didn’t immediately respond to a Bloomberg request for comment. Ukraine has stepped up attacks on Russian energy infrastructure over the past months, targeting refineries, export pipelines and sea terminals to seek to reduce the Kremlin’s oil revenues and its ability to fund the war. If Kyiv’s military forces keep up the strikes before the month ends, November will mark a new peak in the intensity of the campaign to damage Russian refineries.  The strikes are happening as Ukraine is under pressure to agree on a US-led peace deal after an original proposal drew the ire of officials in Kyiv and in Europe. US President Donald Trump earlier this week hailed “tremendous progress” in talks to iron out key issues, while his envoy Steve Witkoff is set to visit Moscow next week to meet with President Vladimir Putin.  The latest raid was the fourth attack on the Saratov refinery this month, according to Bloomberg calculations based on public data and statements. The facility has a design processing capacity of around 140,000 barrels per day.  In total, Russian oil-processing facilities have been hit at least 13 times so far this month, matching a peak in August, Bloomberg calculations show. Russian military forces, meanwhile, have been relentlessly hitting Ukraine’s infrastructure, including residential buildings and utilities, aiming to create lengthy power outages. The prospects for achieving a piece deal are still uncertain,

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Carney Loses Quebec Minister After Energy Deal

Prime Minister Mark Carney lost a cabinet minister over his oil pipeline agreement with Alberta, marking the first major fracture in his Liberal Party caucus over the government’s energy policies. Steven Guilbeault, a former environmental activist with Greenpeace, resigned his position as culture minister but will remain in Parliament. In a statement posted on social media Thursday evening, Guilbeault panned the agreement reached with Alberta, saying the government failed to consult Indigenous communities and the pipeline would pose significant environmental risks. “When I entered politics, it was because I had a deep conviction that I could make a difference in fighting climate change,” he wrote. “My commitment to leaving a better world for the future of our children and our planet remains unchanged.” Guilbeault was first elected to office in 2019, and served as both heritage minister and environment minister under former Prime Minister Justin Trudeau. He also has a high profile in the province of Quebec, a crucial electoral battleground that helped Carney win government in this year’s election. In his letter, Guilbeault noted that the government has dismantled much of his legacy as environment minister — repealing consumer carbon pricing, delaying a policy to encourage zero-emission vehicles and scrapping the oil and gas sector emissions cap. Carney thanked his former minister in a statement late Thursday. “While we may have differing views at times on how exactly we make essential progress, I am glad Steven will continue to offer his important perspectives as a Member of Parliament in our Liberal caucus,” the prime minister said.  Quebec Voice Earlier in the day, Carney had unveiled an agreement with Danielle Smith, premier of the oil-rich province of Alberta. The document pledges federal government support for a new oil pipeline to Canada’s west coast and scraps some Trudeau-era environmental regulations in exchange for Alberta’s agreement to hike

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Two Oil Tankers Suffer Mystery Blasts While in Black Sea

(Update) November 28, 2025, 6:14 PM GMT: Article updated. Two ocean-going tankers that are heavily sanctioned for carrying Russian oil suffered near-simultaneous blasts off Turkey’s Black Sea coast. The first, the 900-foot Kairos, was taking on water after an explosion, according to a local port agent report. Turkey’s Directorate General for Maritime Affairs confirmed the incident and said a second ship, the Virat, had also been struck near its coastline and was billowing smoke. The causes are unclear and a rescue operation for both ships was underway.  The pair are two of hundreds of vessels that were amassed to help keep Russia’s oil moving after it invaded Ukraine. Kairos is sanctioned by the UK and European Union, while Virat was designated by the US and EU.  DENİZCİLİK GENEL MÜDÜRLÜĞÜ@denizcilikgm  VIRAT isimli tanker, Karadeniz’de takribi 35 deniz mili açıkta isabet aldığını bildirmiş, olay yerine kurtarma unsurları ve ticari gemi yönlendirilmiştir. Gemideki 20 personelin durumu iyi olup makine dairesinde yoğun duman tespit edilmiştir. Süreç takip edilmektedir. Sent via Twitter for Android. View original tweet. It’s not the first time that ships linked to Moscow have suffered explosions this year. There was also a spate of blasts in the early months of 2025 that hit merchant ships with a history of calling at Russian ports.  It’s not yet known what happened to these vessels and, if they were attacked, who was responsible. Spain’s navy, which issues navigational warnings in the region, says there’s also a significant risk posed by floating mines in parts of the Black Sea since the conflict began. Kairos is a Suezmax-class vessel whose previous voyage was from the Russian port of Novorossiysk to Paradip in India, hauling Moscow’s flagship crude grade Urals. It was heading back to the Russian port to load its next cargo at the time of the incident,

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Oil Notches Fourth Monthly Drop

Oil posted a fourth monthly loss as traders looked ahead to an OPEC+ meeting this weekend and assessed how the potential of easing geopolitical tensions from Kyiv to Caracas may impact an oversupplied market. West Texas Intermediate edged down to settle below $59 a barrel, after earlier gaining as much as 1.7%, to close out the longest streak of monthly drops since March 2023. The commodity slid to intra-day lows minutes before settlement as The New York Times reported that US President Donald Trump and Venezuelan counterpart Nicolás Maduro discussed a potential meeting in a call last week. A de-escalation between the Trump administration and the oil-rich South American country would sap a major risk premium out of oil prices.  The late-day dip capped off a choppy trading session, marked by thin holiday volumes and an hours-long outage on Chicago Mercantile Exchange’s trading platform that roiled global markets. The halt — which the company said was a result of a cooling issue in a data center — also impacted gasoline and diesel futures that are due to expire on Friday.  OPEC+ nations are set to meet virtually on Sunday and will probably stick with a plan to pause output increases in early 2026, delegates said. With that decision locked in, a key focus may be a long-term review of members’ capacity. US oil has fallen 18% this year, with prices hurt by expectations for a global glut after OPEC+ restarted capacity, while drillers outside the alliance also added supplies.  On Ukraine, Russian President Vladimir Putin said that US President Donald Trump’s proposals for ending Moscow’s war could be the basis for future agreements and expressed an openness to talks, though sticking points that led to stalemates in previous rounds remain. US presidential envoy Steve Witkoff is expected to visit Moscow next week.  The

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CME Futures Outage Disrupts Trading

(Update) November 28, 2025, 11:00 AM GMT: Article updated. Trading of futures and options on the Chicago Mercantile Exchange was halted by a data-center fault, causing hours of disruption to markets across equities, foreign exchange, bonds and commodities. The malfunction was caused by cooling system problems at a data center in the Chicago area, according to facility operator CyrusOne. Engineering teams have restarted several chillers and deployed temporary cooling equipment, a spokesperson said, without giving a time for the resumption of normal operations.  The halt is already longer than a similar, hours-long outage due to a technical error back in 2019 and underscores the reach of CME Group Inc. and its Globex electronic trading platform. It triggered widespread frustration as market participants contemplated the prospect of a lost trading session. Millions of contracts tracking the S&P 500, Dow Jones Industrial Average and Nasdaq 100 trade every weekday virtually around the clock on the CME, one of the world’s largest derivatives exchanges. “It’s a bit like flying dark,” said Thomas Helaine, head of equity sales at TP ICAP Europe in Paris. “When you’re trading cash equity like us, US futures give you an indication of where the market is going before the open. I can only imagine how complicated it must be for derivatives desks.” The outage halted trading of US Treasury futures, while European and UK bond markets that trade on a different exchange were unaffected. EBS, a platform used in foreign exchange, was impacted, hurting price discovery in the market. For some traders, the timing of the disruption on Friday could cause particular inconvenience if it lasts, due to the need to roll positions from one monthly contract to another.  “Traders sitting with a position are certainly quite angry,” said Gnanasekar Thiagarajan, head of trading and hedging strategies at Kaleesuwari Intercontinental. Gold saw

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Petrobras Slumps After Unveiling $109B Spending Plan

Brazilian oil major Petrobras announced a 2% decrease in its next five-year investment plan to $109 billion, putting dividend payments in doubt at a time of lower oil prices. Shares fell. The state-controlled oil producer is caught between the government’s desire to grow the economy – especially ahead of a 2026 presidential election – and investors who demand high dividends and low debt. While Petrobras announced a regular dividend payout of at least $45 billion for the 2026-2030 period, similar to the previous plan, it didn’t commit to pay any extraordinary payouts to shareholders.  Petrobras shares slid as much as 3.4% in Sao Paulo on Friday, the largest intraday drop since August, while Brent prices were are slightly lower. “The absence of short-term capex optimization could result in single-digit dividend yields ,” Itau Unibanco Holding SA said in a note to clients. “This could be perceived as disappointing by investors.” Petroleo Brasileiro SA, as it is formally known, will direct $91 billion of the total capital expenditure to projects under implementation, of which $10 billion will still need budget confirmation subject to a financing analysis. The rest is still under analysis “with a lower degree of maturity,” it said in a filing on Thursday. The spending plan is being closely watched by investors as it has an important political dimension in Brazil. The company is a major source of cash for the federal budget. It is the first time Petrobras has reduced its five-year budget after President Luiz Inacio Lula da Silva took office in 2023.  The previous plan was based on an oil price assumption of $83 a barrel, while Brent crude is currently trading near $63.  Petrobras earmarked 71.6% of the 2026-2030 plan, or $78 billion, for exploration and production. That includes boosting output at its deep-water fields

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Microsoft loses two senior AI infrastructure leaders as data center pressures mount

Microsoft did not immediately respond to a request for comment. Microsoft’s constraints Analysts say the twin departures mark a significant setback for Microsoft at a critical moment in the AI data center race, with pressure mounting from both OpenAI’s model demands and Google’s infrastructure scale. “Losing some of the best professionals working on this challenge could set Microsoft back,” said Neil Shah, partner and co-founder at Counterpoint Research. “Solving the energy wall is not trivial, and there may have been friction or strategic differences that contributed to their decision to move on, especially if they saw an opportunity to make a broader impact and do so more lucratively at a company like Nvidia.” Even so, Microsoft has the depth and ecosystem strength to continue doubling down on AI data centers, said Prabhu Ram, VP for industry research at Cybermedia Research. According to Sanchit Gogia, chief analyst at Greyhound Research, the departures come at a sensitive moment because Microsoft is trying to expand its AI infrastructure faster than physical constraints allow. “The executives who have left were central to GPU cluster design, data center engineering, energy procurement, and the experimental power and cooling approaches Microsoft has been pursuing to support dense AI workloads,” Gogia said. “Their exit coincides with pressures the company has already acknowledged publicly. GPUs are arriving faster than the company can energize the facilities that will house them, and power availability has overtaken chip availability as the real bottleneck.”

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What is Edge AI? When the cloud isn’t close enough

Many edge devices can periodically send summarized or selected inference output data back to a central system for model retraining or refinement. That feedback loop helps the model improve over time while still keeping most decisions local. And to run efficiently on constrained edge hardware, the AI model is often pre-processed by techniques such as quantization (which reduces precision), pruning (which removes redundant parameters), or knowledge distillation (which trains a smaller model to mimic a larger one). These optimizations reduce the model’s memory, compute, and power demands so it can run more easily on an edge device. What technologies make edge AI possible? The concept of the “edge” always assumes that edge devices are less computationally powerful than data centers and cloud platforms. While that remains true, overall improvements in computational hardware have made today’s edge devices much more capable than those designed just a few years ago. In fact, a whole host of technological developments have come together to make edge AI a reality. Specialized hardware acceleration. Edge devices now ship with dedicated AI-accelerators (NPUs, TPUs, GPU cores) and system-on-chip units tailored for on-device inference. For example, companies like Arm have integrated AI-acceleration libraries into standard frameworks so models can run efficiently on Arm-based CPUs. Connectivity and data architecture. Edge AI often depends on durable, low-latency links (e.g., 5G, WiFi 6, LPWAN) and architectures that move compute closer to data. Merging edge nodes, gateways, and local servers means less reliance on distant clouds. And technologies like Kubernetes can provide a consistent management plane from the data center to remote locations. Deployment, orchestration, and model lifecycle tooling. Edge AI deployments must support model-update delivery, device and fleet monitoring, versioning, rollback and secure inference — especially when orchestrated across hundreds or thousands of locations. VMware, for instance, is offering traffic management

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Networks, AI, and metaversing

Our first, conservative, view says that AI’s network impact is largely confined to the data center, to connect clusters of GPU servers and the data they use as they crunch large language models. It’s all “horizontal” traffic; one TikTok challenge would generate way more traffic in the wide area. WAN costs won’t rise for you as an enterprise, and if you’re a carrier you won’t be carrying much new, so you don’t have much service revenue upside. If you don’t host AI on premises, you can pretty much dismiss its impact on your network. Contrast that with the radical metaverse view, our third view. Metaverses and AR/VR transform AI missions, and network services, from transaction processing to event processing, because the real world is a bunch of events pushing on you. They also let you visualize the way that process control models (digital twins) relate to the real world, which is critical if the processes you’re modeling involve human workers who rely on their visual sense. Could it be that the reason Meta is willing to spend on AI, is that the most credible application of AI, and the most impactful for networks, is the metaverse concept? In any event, this model of AI, by driving the users’ experiences and activities directly, demands significant edge connectivity, so you could expect it to have a major impact on network requirements. In fact, just dipping your toes into a metaverse could require a major up-front network upgrade. Networks carry traffic. Traffic is messages. More messages, more traffic, more infrastructure, more service revenue…you get the picture. Door number one, to the AI giant future, leads to nothing much in terms of messages. Door number three, metaverses and AR/VR, leads to a message, traffic, and network revolution. I’ll bet that most enterprises would doubt

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Microsoft’s Fairwater Atlanta and the Rise of the Distributed AI Supercomputer

Microsoft’s second Fairwater data center in Atlanta isn’t just “another big GPU shed.” It represents the other half of a deliberate architectural experiment: proving that two massive AI campuses, separated by roughly 700 miles, can operate as one coherent, distributed supercomputer. The Atlanta installation is the latest expression of Microsoft’s AI-first data center design: purpose-built for training and serving frontier models rather than supporting mixed cloud workloads. It links directly to the original Fairwater campus in Wisconsin, as well as to earlier generations of Azure AI supercomputers, through a dedicated AI WAN backbone that Microsoft describes as the foundation of a “planet-scale AI superfactory.” Inside a Fairwater Site: Preparing for Multi-Site Distribution Efficient multi-site training only works if each individual site behaves as a clean, well-structured unit. Microsoft’s intra-site design is deliberately simplified so that cross-site coordination has a predictable abstraction boundary—essential for treating multiple campuses as one distributed AI system. Each Fairwater installation presents itself as a single, flat, high-regularity cluster: Up to 72 NVIDIA Blackwell GPUs per rack, using GB200 NVL72 rack-scale systems. NVLink provides the ultra-low-latency, high-bandwidth scale-up fabric within the rack, while the Spectrum-X Ethernet stack handles scale-out. Each rack delivers roughly 1.8 TB/s of GPU-to-GPU bandwidth and exposes a multi-terabyte pooled memory space addressable via NVLink—critical for large-model sharding, activation checkpointing, and parallelism strategies. Racks feed into a two-tier Ethernet scale-out network offering 800 Gbps GPU-to-GPU connectivity with very low hop counts, engineered to scale to hundreds of thousands of GPUs without encountering the classic port-count and topology constraints of traditional Clos fabrics. Microsoft confirms that the fabric relies heavily on: SONiC-based switching and a broad commodity Ethernet ecosystem to avoid vendor lock-in and accelerate architectural iteration. Custom network optimizations, such as packet trimming, packet spray, high-frequency telemetry, and advanced congestion-control mechanisms, to prevent collective

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Land & Expand: Hyperscale, AI Factory, Megascale

Land & Expand is Data Center Frontier’s periodic roundup of notable North American data center development activity, tracking the newest sites, land plays, retrofits, and hyperscale campus expansions shaping the industry’s build cycle. October delivered a steady cadence of announcements, with several megascale projects advancing from concept to commitment. The month was defined by continued momentum in OpenAI and Oracle’s Stargate initiative (now spanning multiple U.S. regions) as well as major new investments from Google, Meta, DataBank, and emerging AI cloud players accelerating high-density reuse strategies. The result is a clearer picture of how the next wave of AI-first infrastructure is taking shape across the country. Google Begins $4B West Memphis Hyperscale Buildout Google formally broke ground on its $4 billion hyperscale campus in West Memphis, Arkansas, marking the company’s first data center in the state and the anchor for a new Mid-South operational hub. The project spans just over 1,000 acres, with initial site preparation and utility coordination already underway. Google and Entergy Arkansas confirmed a 600 MW solar generation partnership, structured to add dedicated renewable supply to the regional grid. As part of the launch, Google announced a $25 million Energy Impact Fund for local community affordability programs and energy-resilience improvements—an unusually early community-benefit commitment for a first-phase hyperscale project. Cooling specifics have not yet been made public. Water sourcing—whether reclaimed, potable, or hybrid seasonal mode—remains under review, as the company finalizes environmental permits. Public filings reference a large-scale onsite water treatment facility, similar to Google’s deployments in The Dalles and Council Bluffs. Local governance documents show that prior to the October announcement, West Memphis approved a 30-year PILOT via Groot LLC (Google’s land assembly entity), with early filings referencing a typical placeholder of ~50 direct jobs. At launch, officials emphasized hundreds of full-time operations roles and thousands

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The New Digital Infrastructure Geography: Green Street’s David Guarino on AI Demand, Power Scarcity, and the Next Phase of Data Center Growth

As the global data center industry races through its most frenetic build cycle in history, one question continues to define the market’s mood: is this the peak of an AI-fueled supercycle, or the beginning of a structurally different era for digital infrastructure? For Green Street Managing Director and Head of Global Data Center and Tower Research David Guarino, the answer—based firmly on observable fundamentals—is increasingly clear. Demand remains blisteringly strong. Capital appetite is deepening. And the very definition of a “data center market” is shifting beneath the industry’s feet. In a wide-ranging discussion with Data Center Frontier, Guarino outlined why data centers continue to stand out in the commercial real estate landscape, how AI is reshaping underwriting and development models, why behind-the-meter power is quietly reorganizing the U.S. map, and what Green Street sees ahead for rents, REITs, and the next wave of hyperscale expansion. A ‘Safe’ Asset in an Uncertain CRE Landscape Among institutional investors, the post-COVID era was the moment data centers stepped decisively out of “niche” territory. Guarino notes that pandemic-era reliance on digital services crystallized a structural recognition: data centers deliver stable, predictable cash flows, anchored by the highest-credit tenants in global real estate. Hyperscalers today dominate new leasing and routinely sign 15-year (or longer) contracts, a duration largely unmatched across CRE categories. When compared with one-year apartment leases, five-year office leases, or mall anchor terms, the stability story becomes plain. “These are AAA-caliber companies signing the longest leases in the sector’s history,” Guarino said. “From a real estate point of view, that combination of tenant quality and lease duration continues to position the asset class as uniquely durable.” And development returns remain exceptional. Even without assuming endless AI growth, the math works: strong demand, rising rents, and high-credit tenants create unusually predictable performance relative to

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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.

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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

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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

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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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