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AI vs. endpoint attacks: What security leaders must know to stay ahead

This article is part of VentureBeat’s special issue, “The cyber resilience playbook: Navigating the new era of threats.” Read more from this special issue here. Enterprises run the very real risk of losing the AI arms race to adversaries who weaponize large language models (LLMs) and create fraudulent bots to automate attacks. Trading on the trust of legitimate tools, adversaries are using generative AI to create malware that doesn’t create a unique signature but instead relies on fileless execution, making the attacks often undetectable. Gen AI is extensively being used to create large-scale automated phishing campaigns and automate social engineering, with attackers looking to exploit human vulnerabilities at scale. Gartner points out in its latest Magic Quadrant for Endpoint Protection Platforms that  “leaders in the endpoint protection market are prioritizing integrated security solutions that unify endpoint detection and response (EDR), extended detection and response (XDR) and identity protection into a single platform. This shift enables security teams to reduce complexity while improving threat visibility.” The result? A more complex threat landscape moving at machine speed while enterprise defenders rely on outdated tools and technologies designed for a different era. The scale of these attacks is staggering. Zscaler’s ThreatLabz indicated a nearly 60% year-over-year increase in global phishing attacks, and attributes this rise in part to the proliferation of gen AI-driven schemes. Likewise, Ivanti’s  2024 State of Cybersecurity Report found that 74% of businesses are already seeing the impact of AI-powered threats. And, nine in 10 executives said they believe that AI-powered threats are just getting started. “If you’ve got adversaries breaking out in two minutes, and it takes you a day to ingest data and another day to run a search, how can you possibly hope to keep up?” Elia Zaitsev, CTO of CrowdStrike noted in a recent interview with VentureBeat​. The new cyber arms race: Adversarial AI vs. defensive AI on the endpoint Adversaries, especially cybercrime syndicates and nation-state actors, are refining their tradecraft with AI, adding to their arsenals faster than any enterprise can keep up. Gen AI has democratized how adversaries, from rogue attackers to large-scale cyberwar operations, can create new weapons. “Even if you’re not an expert, gen AI can create scripts or phishing emails on your behalf,” George Kurtz, CrowdStrike CEO and founder at the recent World Economic Forum, said in an interview with CNBC. “It’s never been easier for adversaries. But the good news is, if we properly harness AI on the defensive side, we have a massive opportunity to stay ahead.” As Gartner advises: “AI-enhanced security tools should be viewed as force multipliers rather than standalone replacements for traditional security measures. Organizations must ensure that AI-driven solutions integrate effectively with human decision-making to mitigate risks.” Etay Maor, chief security strategist at Cato Networks, told VentureBeat that “adversaries are not just using AI to automate attacks — they’re using it to blend into normal network traffic, making them harder to detect. The real challenge is that AI-powered attacks are not a single event; they’re a continuous process of reconnaissance, evasion and adaptation.” Cato outlined in its 2024 business highlights how it expanded its secure access service edge (SASE) cloud platform five times in the last year, introducing Cato XDR, Cato endpoint protection platform (EPP), Cato managed SASE, Cato digital experience monitoring (DEM) and Cato IoT/OT Security, all of which aim to streamline and unify security capabilities under one platform. “We’re not just taking share,” said Shlomo Kramer, Cato co-founder and CEO. “We’re redefining how organizations connect and secure their operations, as AI and cloud transform the security landscape.” Unifying endpoints and identities is the future of zero trust. Adversaries are quick to capitalize on unchecked agent sprawl, which is made more unreliable due to a surge in dozens of identities’ data being integral to an endpoint. Using AI to automate reconnaissance at scale, adversaries have an upper hand. All these factors, taken together, set the stage for a new era of AI-powered endpoint security. AI-powered endpoint security ushers in a new era of unified defense Legacy approaches to endpoint security — interdomain trust relationships, assumed trust, perimeter-based security designs, to name a few — are no longer enough. If any network’s security is based on assumed or implied trust, it is as good as breached already. Likewise, relying on static defenses, including antivirus software, perimeter firewalls or, worse, endpoints with dozens of agents loaded on them, leaves an organization just as vulnerable as if they had no cyber defense strategy at all. Gartner observes that: “Identity theft, phishing and data exfiltration are workspace security risks that require further attention. To address these issues, organizations need a holistic workspace security strategy that places the worker at the center of protection and integrates security across device, email, identity, data and application access controls.” Daren Goeson, SVP of unified endpoint management at Ivanti, underscored the growing challenge. “Laptops, desktops, smartphones and IoT devices are essential to modern business, but their expanding numbers create more opportunities for attackers,” he said. “An unpatched vulnerability or outdated software can open the door to serious security risks. But as their numbers grow, so do the opportunities for attackers to exploit them.” To mitigate risks, Goeson emphasizes the importance of centralized security and AI-powered endpoint management. “AI-powered security tools can analyze vast amounts of data, detecting anomalies and predicting threats faster and more accurately than human analysts,” he said. Vineet Arora, CTO at WinWire, agreed: “AI tools excel at rapidly analyzing massive data across logs, endpoints and network traffic, spotting subtle patterns early. They refine their understanding over time — automatically quarantining suspicious activities before significant damage can spread.” Gartner’s recognition of Cato Networks as a Leader in the 2024 Magic Quadrant for Single-Vendor SASE further underscores this industry shift. By delivering networking and security capabilities through a single cloud-based platform, Cato enables organizations to address endpoint threats, identity protection and network security in a unified manner — which is critical in an era when adversaries exploit any gap in visibility. Integrating AI, UEM and zero-trust Experts agree that AI-powered automation enhances threat detection, reducing response times and minimizing security gaps. By integrating AI with unified endpoint management (UEM), businesses gain real-time visibility across devices, users and networks — proactively identifying security gaps before they can be exploited.” By proactively preventing problems, “the strain on IT support is also minimized and employee downtime is drastically reduced,” said Ivanti’s field CISO Mike Riemer. Arora added that, while AI can automate routine tasks and highlight anomalies, “human analysts are critical for complex decisions that require business context — AI should be a force multiplier, not a standalone replacement.” To counter these threats, more organizations are relying on AI to strengthen their zero-trust security frameworks. Zero trust comprises systems that continuously verify every access request while AI actively detects, investigates and, if necessary, neutralizes each threat in real time. Advanced security platforms integrate EDR, XDR and identity protection into a single, intelligent defense system. “When combined with AI, UEM solutions become even more powerful,” said Goeson. “AI-powered endpoint security tools analyze vast datasets to detect anomalies and predict threats faster and more accurately than human analysts. With full visibility across devices, users and networks, these tools proactively identify and close security gaps before they can be exploited.” AI-powered platforms and the growing demand for XDR solutions Nearly all cybersecurity vendors are fast-tracking AI and gen AI-related projects in their DevOps cycles and across their roadmaps. The goal is to enhance threat detection incident response, reduce false positives and create platforms capable of scaling out with full XDR functionality. Vendors in this area include BlackBerry, Bitdefender, Cato Networks, Cisco, CrowdStrike, Deep Instinct, ESET, Fortinet, Ivanti, SentinelOne, Sophos, Trend Micro and Zscaler. Cisco is also pushing a platform-first approach, embedding AI into its security ecosystem. “Security is a data game,” Jeetu Patel, EVP at Cisco, told VentureBeat. “If there’s a platform that only does email, that’s interesting. But if there’s a platform that does email and correlates that to the endpoint, to the network packets and the web, that’s far more valuable.” Nearly every organization interviewed by VentureBeat values XDR for unifying security telemetry across endpoints, networks, identities and clouds. XDR enhances threat detection by correlating signals, boosting efficiency and reducing alert fatigue. Riemer highlighted AI’s defensive shift: “For years, attackers have been utilizing AI to their advantage. However, 2025 will mark a turning point as defenders begin to harness the full potential of AI for cybersecurity purposes.” Riemer noted that AI-driven endpoint security is shifting from reactive to proactive. “AI is already transforming how security teams detect early warning signs of attacks. AI-powered security tools can recognize patterns of device underperformance and automate diagnostics before an issue impacts the business — all with minimal employee downtime and no IT support required.” Arora emphasized: “It’s also crucial for CISOs to assess data handling, privacy and the transparency of AI decision-making before adopting such tools — ensuring they fit both the organization’s compliance requirements and its security strategy.” Cato’s 2024 rollouts exemplify how advanced SASE platforms integrate threat detection, user access controls, and IoT/OT protection in one service. This consolidation reduces complexity for security teams and supports a true zero-trust approach, ensuring continuous verification across devices and networks. Conclusion: Embracing AI-driven security for a new era of threats Adversaries are moving at machine speed, weaponizing gen AI to create sophisticated malware, launch targeted phishing campaigns and circumvent traditional defenses. The takeaway is clear: Legacy endpoint security and patchwork solutions are not enough to protect against threats designed to outmaneuver static defenses. Enterprises must embrace an AI-first strategy that unifies endpoint, identity and network security within a zero-trust framework. AI-powered platforms — built with real-time telemetry, XDR capabilities and predictive intelligence — are the key to detecting and mitigating evolving threats before they lead to a full-on breach. As Kramer put it, “The era of cobbled-together security solutions is over.” Organizations choosing a SASE platform are positioning themselves to proactively combat AI-driven threats. Cato, among other leading providers, underscores that a unified, cloud-native approach — marrying AI with zero-trust principles — will be pivotal in safeguarding enterprises from the next wave of cyber onslaughts.

This article is part of VentureBeat’s special issue, “The cyber resilience playbook: Navigating the new era of threats.” Read more from this special issue here.

Enterprises run the very real risk of losing the AI arms race to adversaries who weaponize large language models (LLMs) and create fraudulent bots to automate attacks.

Trading on the trust of legitimate tools, adversaries are using generative AI to create malware that doesn’t create a unique signature but instead relies on fileless execution, making the attacks often undetectable. Gen AI is extensively being used to create large-scale automated phishing campaigns and automate social engineering, with attackers looking to exploit human vulnerabilities at scale.

Gartner points out in its latest Magic Quadrant for Endpoint Protection Platforms that  “leaders in the endpoint protection market are prioritizing integrated security solutions that unify endpoint detection and response (EDR), extended detection and response (XDR) and identity protection into a single platform. This shift enables security teams to reduce complexity while improving threat visibility.”

The result? A more complex threat landscape moving at machine speed while enterprise defenders rely on outdated tools and technologies designed for a different era.

The scale of these attacks is staggering. Zscaler’s ThreatLabz indicated a nearly 60% year-over-year increase in global phishing attacks, and attributes this rise in part to the proliferation of gen AI-driven schemes. Likewise, Ivanti’s  2024 State of Cybersecurity Report found that 74% of businesses are already seeing the impact of AI-powered threats. And, nine in 10 executives said they believe that AI-powered threats are just getting started.

“If you’ve got adversaries breaking out in two minutes, and it takes you a day to ingest data and another day to run a search, how can you possibly hope to keep up?” Elia Zaitsev, CTO of CrowdStrike noted in a recent interview with VentureBeat​.

The new cyber arms race: Adversarial AI vs. defensive AI on the endpoint

Adversaries, especially cybercrime syndicates and nation-state actors, are refining their tradecraft with AI, adding to their arsenals faster than any enterprise can keep up. Gen AI has democratized how adversaries, from rogue attackers to large-scale cyberwar operations, can create new weapons.

“Even if you’re not an expert, gen AI can create scripts or phishing emails on your behalf,” George Kurtz, CrowdStrike CEO and founder at the recent World Economic Forum, said in an interview with CNBC. “It’s never been easier for adversaries. But the good news is, if we properly harness AI on the defensive side, we have a massive opportunity to stay ahead.”

As Gartner advises: “AI-enhanced security tools should be viewed as force multipliers rather than standalone replacements for traditional security measures. Organizations must ensure that AI-driven solutions integrate effectively with human decision-making to mitigate risks.”

Etay Maor, chief security strategist at Cato Networks, told VentureBeat that “adversaries are not just using AI to automate attacks — they’re using it to blend into normal network traffic, making them harder to detect. The real challenge is that AI-powered attacks are not a single event; they’re a continuous process of reconnaissance, evasion and adaptation.”

Cato outlined in its 2024 business highlights how it expanded its secure access service edge (SASE) cloud platform five times in the last year, introducing Cato XDR, Cato endpoint protection platform (EPP), Cato managed SASE, Cato digital experience monitoring (DEM) and Cato IoT/OT Security, all of which aim to streamline and unify security capabilities under one platform. “We’re not just taking share,” said Shlomo Kramer, Cato co-founder and CEO. “We’re redefining how organizations connect and secure their operations, as AI and cloud transform the security landscape.”

Unifying endpoints and identities is the future of zero trust. Adversaries are quick to capitalize on unchecked agent sprawl, which is made more unreliable due to a surge in dozens of identities’ data being integral to an endpoint. Using AI to automate reconnaissance at scale, adversaries have an upper hand.

All these factors, taken together, set the stage for a new era of AI-powered endpoint security.

AI-powered endpoint security ushers in a new era of unified defense

Legacy approaches to endpoint security — interdomain trust relationships, assumed trust, perimeter-based security designs, to name a few — are no longer enough. If any network’s security is based on assumed or implied trust, it is as good as breached already.

Likewise, relying on static defenses, including antivirus software, perimeter firewalls or, worse, endpoints with dozens of agents loaded on them, leaves an organization just as vulnerable as if they had no cyber defense strategy at all.

Gartner observes that: “Identity theft, phishing and data exfiltration are workspace security risks that require further attention. To address these issues, organizations need a holistic workspace security strategy that places the worker at the center of protection and integrates security across device, email, identity, data and application access controls.”

Daren Goeson, SVP of unified endpoint management at Ivanti, underscored the growing challenge. “Laptops, desktops, smartphones and IoT devices are essential to modern business, but their expanding numbers create more opportunities for attackers,” he said. “An unpatched vulnerability or outdated software can open the door to serious security risks. But as their numbers grow, so do the opportunities for attackers to exploit them.”

To mitigate risks, Goeson emphasizes the importance of centralized security and AI-powered endpoint management. “AI-powered security tools can analyze vast amounts of data, detecting anomalies and predicting threats faster and more accurately than human analysts,” he said.

Vineet Arora, CTO at WinWire, agreed: “AI tools excel at rapidly analyzing massive data across logs, endpoints and network traffic, spotting subtle patterns early. They refine their understanding over time — automatically quarantining suspicious activities before significant damage can spread.”

Gartner’s recognition of Cato Networks as a Leader in the 2024 Magic Quadrant for Single-Vendor SASE further underscores this industry shift. By delivering networking and security capabilities through a single cloud-based platform, Cato enables organizations to address endpoint threats, identity protection and network security in a unified manner — which is critical in an era when adversaries exploit any gap in visibility.

Integrating AI, UEM and zero-trust

Experts agree that AI-powered automation enhances threat detection, reducing response times and minimizing security gaps. By integrating AI with unified endpoint management (UEM), businesses gain real-time visibility across devices, users and networks — proactively identifying security gaps before they can be exploited.”

By proactively preventing problems, “the strain on IT support is also minimized and employee downtime is drastically reduced,” said Ivanti’s field CISO Mike Riemer.

Arora added that, while AI can automate routine tasks and highlight anomalies, “human analysts are critical for complex decisions that require business context — AI should be a force multiplier, not a standalone replacement.”

To counter these threats, more organizations are relying on AI to strengthen their zero-trust security frameworks. Zero trust comprises systems that continuously verify every access request while AI actively detects, investigates and, if necessary, neutralizes each threat in real time. Advanced security platforms integrate EDR, XDR and identity protection into a single, intelligent defense system.

“When combined with AI, UEM solutions become even more powerful,” said Goeson. “AI-powered endpoint security tools analyze vast datasets to detect anomalies and predict threats faster and more accurately than human analysts. With full visibility across devices, users and networks, these tools proactively identify and close security gaps before they can be exploited.”

AI-powered platforms and the growing demand for XDR solutions

Nearly all cybersecurity vendors are fast-tracking AI and gen AI-related projects in their DevOps cycles and across their roadmaps. The goal is to enhance threat detection incident response, reduce false positives and create platforms capable of scaling out with full XDR functionality. Vendors in this area include BlackBerry, Bitdefender, Cato Networks, Cisco, CrowdStrike, Deep Instinct, ESET, Fortinet, Ivanti, SentinelOne, Sophos, Trend Micro and Zscaler.

Cisco is also pushing a platform-first approach, embedding AI into its security ecosystem. “Security is a data game,” Jeetu Patel, EVP at Cisco, told VentureBeat. “If there’s a platform that only does email, that’s interesting. But if there’s a platform that does email and correlates that to the endpoint, to the network packets and the web, that’s far more valuable.”

Nearly every organization interviewed by VentureBeat values XDR for unifying security telemetry across endpoints, networks, identities and clouds. XDR enhances threat detection by correlating signals, boosting efficiency and reducing alert fatigue.

Riemer highlighted AI’s defensive shift: “For years, attackers have been utilizing AI to their advantage. However, 2025 will mark a turning point as defenders begin to harness the full potential of AI for cybersecurity purposes.”

Riemer noted that AI-driven endpoint security is shifting from reactive to proactive. “AI is already transforming how security teams detect early warning signs of attacks. AI-powered security tools can recognize patterns of device underperformance and automate diagnostics before an issue impacts the business — all with minimal employee downtime and no IT support required.”

Arora emphasized: “It’s also crucial for CISOs to assess data handling, privacy and the transparency of AI decision-making before adopting such tools — ensuring they fit both the organization’s compliance requirements and its security strategy.”

Cato’s 2024 rollouts exemplify how advanced SASE platforms integrate threat detection, user access controls, and IoT/OT protection in one service. This consolidation reduces complexity for security teams and supports a true zero-trust approach, ensuring continuous verification across devices and networks.

Conclusion: Embracing AI-driven security for a new era of threats

Adversaries are moving at machine speed, weaponizing gen AI to create sophisticated malware, launch targeted phishing campaigns and circumvent traditional defenses. The takeaway is clear: Legacy endpoint security and patchwork solutions are not enough to protect against threats designed to outmaneuver static defenses.

Enterprises must embrace an AI-first strategy that unifies endpoint, identity and network security within a zero-trust framework. AI-powered platforms — built with real-time telemetry, XDR capabilities and predictive intelligence — are the key to detecting and mitigating evolving threats before they lead to a full-on breach.

As Kramer put it, “The era of cobbled-together security solutions is over.” Organizations choosing a SASE platform are positioning themselves to proactively combat AI-driven threats. Cato, among other leading providers, underscores that a unified, cloud-native approach — marrying AI with zero-trust principles — will be pivotal in safeguarding enterprises from the next wave of cyber onslaughts.

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SoftBank to buy Ampere for $6.5B, fueling Arm-based server market competition

SoftBank’s announcement suggests Ampere will collaborate with other SBG companies, potentially creating a powerful ecosystem of Arm-based computing solutions. This collaboration could extend to SoftBank’s numerous portfolio companies, including Korean/Japanese web giant LY Corp, ByteDance (TikTok’s parent company), and various AI startups. If SoftBank successfully steers its portfolio companies toward Ampere processors, it could accelerate the shift away from x86 architecture in data centers worldwide. Questions remain about Arm’s server strategy The acquisition, however, raises questions about how SoftBank will balance its investments in both Arm and Ampere, given their potentially competing server CPU strategies. Arm’s recent move to design and sell its own server processors to Meta signaled a major strategic shift that already put it in direct competition with its own customers, including Qualcomm and Nvidia. “In technology licensing where an entity is both provider and competitor, boundaries are typically well-defined without special preferences beyond potential first-mover advantages,” Kawoosa explained. “Arm will likely continue making independent licensing decisions that serve its broader interests rather than favoring Ampere, as the company can’t risk alienating its established high-volume customers.” Industry analysts speculate that SoftBank might position Arm to focus on custom designs for hyperscale customers while allowing Ampere to dominate the market for more standardized server processors. Alternatively, the two companies could be merged or realigned to present a unified strategy against incumbents Intel and AMD. “While Arm currently dominates processor architecture, particularly for energy-efficient designs, the landscape isn’t static,” Kawoosa added. “The semiconductor industry is approaching a potential inflection point, and we may witness fundamental disruptions in the next 3-5 years — similar to how OpenAI transformed the AI landscape. SoftBank appears to be maximizing its Arm investments while preparing for this coming paradigm shift in processor architecture.”

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Nvidia, xAI and two energy giants join genAI infrastructure initiative

The new AIP members will “further strengthen the partnership’s technology leadership as the platform seeks to invest in new and expanded AI infrastructure. Nvidia will also continue in its role as a technical advisor to AIP, leveraging its expertise in accelerated computing and AI factories to inform the deployment of next-generation AI data center infrastructure,” the group’s statement said. “Additionally, GE Vernova and NextEra Energy have agreed to collaborate with AIP to accelerate the scaling of critical and diverse energy solutions for AI data centers. GE Vernova will also work with AIP and its partners on supply chain planning and in delivering innovative and high efficiency energy solutions.” The group claimed, without offering any specifics, that it “has attracted significant capital and partner interest since its inception in September 2024, highlighting the growing demand for AI-ready data centers and power solutions.” The statement said the group will try to raise “$30 billion in capital from investors, asset owners, and corporations, which in turn will mobilize up to $100 billion in total investment potential when including debt financing.” Forrester’s Nguyen also noted that the influence of two of the new members — xAI, owned by Elon Musk, along with Nvidia — could easily help with fundraising. Musk “with his connections, he does not make small quiet moves,” Nguyen said. “As for Nvidia, they are the face of AI. Everything they do attracts attention.” Info-Tech’s Bickley said that the astronomical dollars involved in genAI investments is mind-boggling. And yet even more investment is needed — a lot more.

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IBM broadens access to Nvidia technology for enterprise AI

The IBM Storage Scale platform will support CAS and now will respond to queries using the extracted and augmented data, speeding up the communications between GPUs and storage using Nvidia BlueField-3 DPUs and Spectrum-X networking, IBM stated. The multimodal document data extraction workflow will also support Nvidia NeMo Retriever microservices. CAS will be embedded in the next update of IBM Fusion, which is planned for the second quarter of this year. Fusion simplifies the deployment and management of AI applications and works with Storage Scale, which will handle high-performance storage support for AI workloads, according to IBM. IBM Cloud instances with Nvidia GPUs In addition to the software news, IBM said its cloud customers can now use Nvidia H200 instances in the IBM Cloud environment. With increased memory bandwidth (1.4x higher than its predecessor) and capacity, the H200 Tensor Core can handle larger datasets, accelerating the training of large AI models and executing complex simulations, with high energy efficiency and low total cost of ownership, according to IBM. In addition, customers can use the power of the H200 to process large volumes of data in real time, enabling more accurate predictive analytics and data-driven decision-making, IBM stated. IBM Consulting capabilities with Nvidia Lastly, IBM Consulting is adding Nvidia Blueprint to its recently introduced AI Integration Service, which offers customers support for developing, building and running AI environments. Nvidia Blueprints offer a suite pre-validated, optimized, and documented reference architectures designed to simplify and accelerate the deployment of complex AI and data center infrastructure, according to Nvidia.  The IBM AI Integration service already supports a number of third-party systems, including Oracle, Salesforce, SAP and ServiceNow environments.

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Nvidia’s silicon photonics switches bring better power efficiency to AI data centers

Nvidia typically uses partnerships where appropriate, and the new switch design was done in collaboration with multiple vendors across different aspects, including creating the lasers, packaging, and other elements as part of the silicon photonics. Hundreds of patents were also included. Nvidia will licensing the innovations created to its partners and customers with the goal of scaling this model. Nvidia’s partner ecosystem includes TSMC, which provides advanced chip fabrication and 3D chip stacking to integrate silicon photonics into Nvidia’s hardware. Coherent, Eoptolink, Fabrinet, and Innolight are involved in the development, manufacturing, and supply of the transceivers. Additional partners include Browave, Coherent, Corning Incorporated, Fabrinet, Foxconn, Lumentum, SENKO, SPIL, Sumitomo Electric Industries, and TFC Communication. AI has transformed the way data centers are being designed. During his keynote at GTC, CEO Jensen Huang talked about the data center being the “new unit of compute,” which refers to the entire data center having to act like one massive server. That has driven compute to be primarily CPU based to being GPU centric. Now the network needs to evolve to ensure data is being fed to the GPUs at a speed they can process the data. The new co-packaged switches remove external parts, which have historically added a small amount of overhead to networking. Pre-AI this was negligible, but with AI, any slowness in the network leads to dollars being wasted.

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Critical vulnerability in AMI MegaRAC BMC allows server takeover

“In disruptive or destructive attacks, attackers can leverage the often heterogeneous environments in data centers to potentially send malicious commands to every other BMC on the same management segment, forcing all devices to continually reboot in a way that victim operators cannot stop,” the Eclypsium researchers said. “In extreme scenarios, the net impact could be indefinite, unrecoverable downtime until and unless devices are re-provisioned.” BMC vulnerabilities and misconfigurations, including hardcoded credentials, have been of interest for attackers for over a decade. In 2022, security researchers found a malicious implant dubbed iLOBleed that was likely developed by an APT group and was being deployed through vulnerabilities in HPE iLO (HPE’s Integrated Lights-Out) BMC. In 2018, a ransomware group called JungleSec used default credentials for IPMI interfaces to compromise Linux servers. And back in 2016, Intel’s Active Management Technology (AMT) Serial-over-LAN (SOL) feature which is part of Intel’s Management Engine (Intel ME), was exploited by an APT group as a covert communication channel to transfer files. OEM, server manufacturers in control of patching AMI released an advisory and patches to its OEM partners, but affected users must wait for their server manufacturers to integrate them and release firmware updates. In addition to this vulnerability, AMI also patched a flaw tracked as CVE-2024-54084 that may lead to arbitrary code execution in its AptioV UEFI implementation. HPE and Lenovo have already released updates for their products that integrate AMI’s patch for CVE-2024-54085.

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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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