Enterprises Rebuild AI Infrastructure as Autonomous Agents Force Private Cloud Governance Overhaul

AI factory automation brings production AI to private cloud

From metal to model: Private cloud gets an assembly line for production AI Enterprises moving artificial intelligence from pilot projects into production are discovering that the hard part is no longer the model. It's the infrastructure beneath it. Cost, tokenomics, data privacy and the manual labor of stitching together graphics processing units, servers, networking and software stacks have become gating factors for deployment at scale. Those pressures are pushing more production AI workloads back into the data center, where organizations can keep models close to their data. Broadcom Inc. is betting that turnkey automation can simplify that shift, according to Prashanth Shenoy (pictured, right), chief marketing officer and vice president of the VMware Cloud Foundation division at Broadcom. "A lot of our customers are looking at private cloud in an on-premises environment to deploy their production AI workloads at scale. But as they've been trying to do this, it's been a very complex process from what we call the metal to model," said Shenoy. "Setting up GPUs, servers, networking, Kubernetes, containers, AI software stack, testing, validating which models to use. It's an extremely manual and complex process." Shenoy and Raghu Nambiar (left), corporate vice president of software and solutions at Advanced Micro Devices Inc., spoke with theCUBE Research's Christophe Bertrand and co-host Alison Kosik at VMware Explore, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed how validated AMD-accelerated VMware Cloud Foundation infrastructure could provide a more automated and flexible path for deploying AI models alongside existing applications. (*Disclosure below.) Hardware choice underpins the AI factory model Operational automation is one half of Broadcom's AI factory pitch; hardware flexibility is the other. That matters because most enterprises already run traditional and container workloads on the same platform and want AI to arrive without a parallel infrastructure to manage alongside it, Shenoy noted. "They already have the AI factory built in with VCF," he said. "We have automated this to do a lot simpler way of deploying." AMD's contribution spans both compute tiers, with the recently launched MI350P PCIe accelerator aimed at enterprises starting out. The company has more than 1,600 vSAN ReadyNodes in market across major server suppliers, Nambiar noted, and sizing guidance now follows model scale. "If your problem size is 10 billion parameters, CPU is the answer," he said. "But if you're looking at the 100 billion parameters range, then MI350P is the answer. If you have a larger model, 1 trillion plus, then MI355X is the answer." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of VMware Explore: (* Disclosure: TheCUBE is a paid media partner for VMware Explore event. Neither Broadcom, the sponsor of theCUBE's event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Infrastructure software targets shadow AI in the packet path

The packet path becomes the place to catch shadow AI before it spreads Enterprise infrastructure software is being redrawn around a user that never sleeps, never logs off and can act thousands of times a minute. As autonomous agents move from pilots into production, the controls built for human employees are proving a poor fit for machine identities operating at speed. That shift is turning the network and identity layers into the front line for agentic deployments, a theme running through much of this year's VMware Explore coverage. Securing those workloads has to start before the first agent ships, according to Umesh Mahajan (pictured, left), vice president and general manager of the Application Networking and Security division at Broadcom Inc. "If you don't have security, you really can't go deploy agentic AI," said Mahajan. "First, you have to make sure that the agentic AI workloads don't get compromised from the outside. Then, we see the agentic AI workloads can themselves go rogue and start attacking outwards, so you need security both inwards and outwards." Mahajan and Clayton Donley (right), vice president and general manager of the Identity Management Security division at Broadcom, spoke with theCUBE's Christophe Bertrand and co-host Alison Kosik at VMware Explore, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. Identity and network controls converge in infrastructure software Decades of access management were designed for people, and that inheritance does not transfer cleanly to software that acts on its own behalf. Broadcom's answer includes AgentMinder, which pairs signed agent identities with runtime inspection of application programming interface traffic and observability, Donley explained. "We've had 50 years of figuring out how to manage your employees or customers or other people accessing your computer systems," Donley said. "We've had about 15 minutes to figure out how to do it for AI agents. The problem is that we're giving these agents more and more autonomy, more ability to do things on their own without those controls." Sitting in the packet path lets the network tier discover Model Context Protocol servers, agents and models, then flag unauthorized ones as shadow AI, Mahajan noted. Putting that together from separate products is where customers stumble, which is why he advocates for a single infrastructure software stack. "If we make a customer stitch together multiple products, they have an awful time; they make mistakes," Mahajan said. "Having a curated security stack completely from Broadcom goes a long way in securing them." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of VMware Explore:

Agentic AI pushes enterprises to rebuild private cloud

Private cloud grows up as enterprises push AI into production Enterprise infrastructure is being rebuilt around agentic AI, and the center of gravity is shifting back toward private environments where data, cost and control sit under one roof. As inference workloads scale, the questions facing information technology teams are less about model choice and more about where those workloads actually run. That shift is the backdrop for Broadcom Inc.'s pitch at this week's VMware Explore, where private cloud, private AI and agent security dominate the agenda. Behavior and security of autonomous agents -- not raw compute -- now represent the hardest part of the problem, according to Christophe Bertrand (pictured, left), analyst in residence - cyber resiliency, data protection, data management at theCUBE Research. "Agentic AI is super critical to the infrastructure. And I think the biggest problem beyond data and access to data is the behavior and the security of these agents," said Bertrand. "Another component, I just mentioned data, is governance ... governance, compliance and sovereign cloud." Bertrand and co-host Alison Kosik (left), previewed the show's key themes during theCUBE's coverage of VMware Explore, SiliconANGLE Media's livestreaming studio. Agentic AI puts the economics of private cloud back in play The infrastructure conversation has moved quickly. With VMware Cloud Foundation 9.1 landing on top of a 9.0 upgrade cycle many customers are still working through, partners are being pulled into implementation work while an AI Factory ecosystem takes shape around chipmakers and server suppliers. The cost side of that build-out is where the technical detail gets translated for buyers, Bertrand noted. "We'll talk about some economics and a very interesting discussion I'm expecting to have around advanced memory tiering," Bertrand said. "I think we're going to be able to translate that into economics very quickly and performance at scale for this evolution into an AI world, leveraging private clouds more aggressively." Adoption data comparing private cloud inference with public cloud runs is also expected to surface during the event, alongside customer security use cases and a discussion of how AI reshapes network requirements. For Bertrand, the through-line is an ecosystem proving it can carry production workloads. "We're going to see that VMware technology is front and center when it comes to AI transformation," Bertrand said. "And we're going to see that the topics around agentic AI, around economics, around how to really evolve into this new world, both for partners supporting customers and end users, it's going to become a lot clearer." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of VMware Explore:

VMware Adds Google, Nvidia AI Models To VCF; Boosts Tanzu Security To Drive AI Adoption

'The more that VMware puts together easier bundling and makes it easier for our customers to operationalize AI -- the more we see less barriers for adoption,' says Bob Keblusek, CTO at VMware partner Sentinel Technologies. VMware has added many of the world's most popular AI models to its VMware Cloud Foundation (VCF) private cloud platform to enable partners to bring more models on-premises and provide model-as-a-service for customers. Some of the newest AI models from Google, Nvidia, NEC, Alibaba Cloud and Z.ai have now been tested and validated to run on VCF. "The more that VMware puts together easier bundling and makes it easier for our customers to operationalize AI -- the more we see less barriers for adoption," said Bob Keblusek, CTO at VMware partner Sentinel Technologies. [Related: VMware AgentMinder And New Agentic AI Security An 'Amazing Story' For Customers, 11:11 Systems Explains] Keblusek said VMware is giving clients a clear path to data sovereignty and cost-effective AI at scale, with leading models securely available and delivered as a service via VCF. "For Sentinel's part, we have cost optimization dashboards and some FinOps basically around tokenization because it starts to really accelerate as you're adopting AI, especially if you're using frontier models," said Keblusek. "So a lot of the use cases can be localized." Google Gemma 4, Nvidia Nemotron 3 And Other AI Models Now On VCF Two of the most popular AI models now validated on VCF include Google's Gemma 4 and Nvidia's Nemotron 3. Gemma 4 is Google's open-source, open-weight multimodal model family, purpose-built for developers and researchers -- enabling enterprises to build and deploy autonomous AI agents. Nvidia's Nemotron 3 family of open, multimodal models aim to deliver accuracy and efficiency to help agents complete tasks faster. VMware said by combining hybrid Mamba-Transformer MoE architecture, 1 million context and multi-environment reinforcement learning, Nemotron 3 enables long-running agentic workflows across enterprise applications. Another new AI model for VCF includes Z.ai's GLM 5.2 open-source General Language Model for deploying coding and reasoning agents locally for multistep autonomous workflows with data sovereignty. The final two new AI models for VCF include NEC's cotomi model and Alibaba's Qwen 3.8-27B open-weight model. Over 150 Open-Source AI Models Now On VCF VMware said customers now have the ability to run more than 150 open-source models on VCF. Keblusek said VMware is committed to giving customers a broad set of AI models for their on-premises infrastructure, all validated on VCF. "It's good to be able to localize and have control over that, which you don't have in some cases, depending on the product," he said, adding that VCF can run inference workloads, agentic applications, containerized services and traditional VMs together, eliminating the need to manage separate stacks. VMware Boosts Tanzu With New Security Innovation The new AI models on VCF were unveiled this week at VMware Explore 2026 in Las Vegas. Another huge announcement was that VMware launched new AI-ready data foundations for the VMware Tanzu Platform. The update provides an end-to-end framework enabling enterprises to transition safely from initial AI pilots to fully production-ready AI agents inside their own secure private clouds. New capabilities included hardened agent sandboxes that enforced a "deny-by-default" security containment model that isolates credentials, helping to prevent prompt injection attacks and unauthorized network access. "With agentic AI, it's about how do you govern that, control that, orchestrate that, which are very large topics for us with our customers who are adopting agentic -- it's a large conversation that many vendors are taking a swing at with different approaches to handling it," said Keblusek. "Seeing VMware go after this with the Tanzu platform makes a ton of sense. If we're able to add agentic security so that our customers can adopt agentic and feel comfortable about it, run it in their own data centers or in a VMware secure enclave or cloud -- that is all very welcomed as security is the top concern when talking to customers about AI adoption," he said. Another new feature is the AI-ready data foundations that process structured and unstructured enterprise data on-site, delivering high-precision context to AI agents to improve accuracy, reduce hallucinations and lower token costs. Other new innovations include an out-of-the-box developer harness that accelerates build times with preapproved skills, step-by-step workflows, human-in-the-loop controls and integrated memory services. Lastly, VMware launched a curated marketplace that operates a centralized catalog where developers and agents can safely discover and connect to vetted AI models, tools and data products. "We need to really put the controls in place to enforce policies, and it starts with monitoring, which is what VMware is doing," Keblusek said. "It starts with being able to build that secure, trustworthy infrastructure, and then we'll see AI adopted at even higher rates than we see today thanks to VMware innovation."

AI infrastructure governance shifts as agents go rogue

Rogue agents are forcing a governance reckoning as enterprises hand over the keys Governance is moving into the foundations of enterprise AI infrastructure as autonomous agents graduate from experiments to mission-critical work. Companies that spent decades refining controls for human employees now manage a second workforce that has no badge number, no paycheck and no moral compass -- and no track record to audit. That governance gap is dominating security conversations across the private cloud market, where agents are being handed corporate data, application programming interfaces and the ability to act without supervision. But business units are not waiting for information technology teams to catch up, according to Clayton Donley (pictured), vice president and general manager of the Identity Management Security Division at Broadcom Inc. "We talk to companies every day that are doing mission-critical things very quickly with [AI]," Donley said. "It's not happening in an environment where we have 50 years of figuring out how to deal with employees and giving them their rights. It's happening in a brand new world." Donley spoke with theCUBE's John Furrier at VMware Explore 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed agent identity, governance and the controls needed to run autonomous systems safely at scale. (* Disclosure below.) Agent identity becomes the control point for AI infrastructure Early anxiety around agents centered on attackers wielding them against the enterprise. Attention has since turned to the opposite risk -- an organization's own agents operating outside any certification or audit regime, a gap that regulated industries cannot carry for long, Donley explained. "With Sarbanes-Oxley, back in the day, you used to have to certify that your employees had [appropriate] access. Nobody certifies [that] my agents have this access. Nobody does any of that," he said. "The maturity's not there, but what we're seeing is a trend to try to pick up that maturity." Closing that gap means treating agents as identities first. Broadcom, which has been reworking VMware security for the agentic era, is applying decades of distributed application tracing to prompts and tool calls, extending observability into what an agent actually did and why, Donley noted. Three principles are fundamental: identity, intervention and inspection. "[You need] the identity of the agent, the control point to choke off bad things from happening and then being able to monitor what is actually happening," Donley said. "Being able to tie it together is really critical." Enterprises can layer that control onto existing AI infrastructure rather than rebuilding it, he added. The starting point is passive: Watch the traffic, identify the agents and then introduce a central control point where policy can be enforced. "Sometimes the starting thing we do is we just watch the traffic, because it's very easy, it's very cheap, it doesn't require you to change anything," Donley said. "You take away their Claude key, you take away their OpenAI key, and you give them a key to yours. Now you can make sure they can't circumvent you by using their keys through some other app." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of VMware Explore 2026: (* Disclosure: TheCUBE is a paid media partner for the VMware Explore 2026 event. Neither Broadcom, the sponsor of theCUBE's event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

VMware Explore 2026: 5 Biggest AI, VCF And Security Launches

From VMware AI Factory and new AI models for VMware Cloud Foundation to Broadcom's new AgentMinder, here are the five biggest launches at VMware Explore 2026 that you need to know about. Thousands of VMware partners and customers are flocking to Las Vegas this week for VMware Explore 2026 to learn about new products, strategy and VMware's AI future. VMware by Broadcom is expected to launch its new VMware AI Factory alongside VMware Cloud Foundation (VCF) and is adding new AI model validations from Google, Nvidia and others for VCF. Additionally, the Palo Alto, Calif.-based private cloud and software star is unleashing three new VMware Private AI services to help make AI operational, governable and cost-effective. [Related: Broadcom, BMC And IBM Top Gartner's List For Best Automation And Orchestration Platforms Of 2026] VMware Explore 2026; 'Innovate Forward' In an interview with CRN, Broadcom's VCF leader Krish Prasad said AI Factory and other VMware Explore launches are "going to be huge" for partners and customers. "At Explore, we're talking about innovating forward, meaning if you go back and think about some of the innovations that we brought to market -- like memory tiering, like GPU enablement as part of our platform -- we did that before the big need was there in the market and before the [supply] crisis came about," Prasad, senior vice president and general manager of VCF, told CRN. "So customers had ready solutions from us before they hit the crisis. So that's our mantra, which is innovate forward," he added. Prasad said VMware Explore 2026 launches provide clients solutions to what's "coming down the road" so partners can have the "innovations ready" when businesses realize they need it. CRN breaks down the five biggest launches at VMware Explore in Las Vegas today that you need to know about.

Broadcom Unveils VMware Private AI Cloud for Secure, Cost-Effective Enterprise AI

Broadcom's Portfolio of Advanced Cloud Infrastructure, Application and Security Software Gives Enterprises a Production-ready Path to Building, Running, and Governing AI Where Their Data Lives Broadcom Inc today introduced VMware Private AI Cloud, a more secure, scalable, and flexible approach to AI that brings the model to the data, not the data to the model. Built on Broadcom's advanced software capabilities, VMware Private AI Cloud gives organizations a production-ready path to securely building, running, and governing inference workloads, agentic applications, and traditional enterprise workloads together on a single private cloud platform. Offering diverse hardware, model, and accelerator choices, VMware Private AI Cloud lets enterprises scale AI cost-effectively, operate more securely, and innovate rapidly. "VMware Private AI Cloud is the inflection point where enterprise private cloud and private AI infrastructure stop operating as separate disciplines and become one -- enabling production inference workloads and agentic AI with the data sovereignty, compliance posture, and cost predictability their business demands," said Ram Velaga, president, Infrastructure Software Group, Broadcom. Scale AI Cost-Effectively VMware Private AI Cloud addresses the three core AI cost drivers: hardware CapEx, operational complexity, and token economics (tokenomics). VMware Cloud Foundation (VCF) 9 lowers hardware costs via NVMe memory tiering and cluster-wide storage deduplication. VCF supports GPUs, CPUs, and accelerators from leading vendors, along with server hardware from major OEM and ODM vendors, allowing customers to run heterogeneous clusters cost-effectively. To optimize tokenomics and resource usage, it features token monitoring, multi-tenant model sharing, enhanced GPU/vGPU tracking, and an AI metrics observability dashboard. Infrastructure and operations innovations announced for Private AI Cloud include: * Metal to model faster with VMware AI Factory: Broadcom announced VMware AI Factory, the software-defined foundation of VMware Private AI Cloud, providing customers a simplified path to production with new automation innovations for deploying AI-ready infrastructure and Day 2 operations. With VMware AI Factory, customers can achieve faster time to first model deployment and better manage AI tokenomics. * Validated AI Models for VCF enable Model as a Service: VMware AI Factory gives enterprises a production-ready path to running leading AI models on-premises. VCF customers can run more than 150 open source and commercial models, including Nemotron 3, Gemma 4, cotomi, Qwen, and GLM 5.2. Broadcom is working with the world's leading AI model providers to give organizations a clear path to data sovereignty and cost-effective AI at scale, with leading models available securely and delivered as a service to their user community through VCF's built-in services. Operate More Securely Designed with a defense-in-depth approach aligned to NIST CSF 2.0, VCF protects against AI-accelerated threats by minimizing the attack surface and enabling continuous compliance. Automated, non-disruptive updates keep systems current, while VMware vDefend uses virtual patching and hypervisor-level lateral security with microsegmentation to enforce Zero Trust and block exploits. Furthermore, vDefend's multi-layer threat defense and VMware Avi Load Balancer's web application firewall and API protection prevent sophisticated attacks. Security innovations supporting Private AI Cloud include: * TrueSource by Broadcom for verifiably built open source: Designed to address the acceleration in AI-enabled exploitation, Spring Enterprise delivers secure, curated Spring releases from the team that maintains it, including frontier model scanned, human-verified patches delivered simultaneously across every release line. TrueSource Trusted Artifacts extends clean-room builds across the Java ecosystem, Python, and Node.js, along with the Bitnami Secure Images catalog while TrueSource Data Services brings the same standard to the data tier: PostgreSQL, RabbitMQ, MySQL, and Valkey. * Agentic Zero Trust with vDefend: New vDefend enhancements will extend Zero Trust lateral security for agentic AI workloads by identifying agentic AI components through continuous monitoring of traffic flows, detecting unauthorized usage of shadow AI, and providing distributed virtual patching through AI-generated Intrusion Detection and Prevention (IDPS) signatures. * Agentic Threat Defense with Avi Load Balancer: Avi web security will broaden its protection of agentic AI workloads by restricting agents from accessing unauthorized tools and preventing misuse; flagging and isolating anomalous behavior to detect zero day attacks; and helping to prevent the unauthorized exfiltration of sensitive data with data protection guardrails. Innovate Rapidly for the Agentic AI Era Unlike traditional apps, autonomous AI agents can act unchecked, exceed scope, or misinterpret instructions. Consequently, trust depends on robust controls and data integrity. VMware Tanzu Platform, with VMware vDefend, provides a foundation for trustworthy enterprise agents via a deny-by-default architecture, a prebuilt harness, and a curated marketplace. Agentic AI innovations announced for Private AI Cloud include: * AgentMinder by Broadcom helps govern autonomous AI agents at scale: Broadcom today unveiled AgentMinder, a new solution that provides enterprises with a central control plane for autonomous AI agents. It treats agents as enterprise-grade identities, binding their authority to a specific mission, approved tools, and authorized resources. Additionally, it provides runtime policy enforcement -- ensuring least-privileged access for every tool invocation -- and delivers compliance-grade auditability, giving enterprises full visibility into what their agents are doing. * AI-ready data foundations turn enterprise data into AI agent knowledge: New AI-ready data foundations in Tanzu Platform let data owners build and manage dynamic pipelines across structured and unstructured data, producing data products optimized for low-cost agent consumption. Those products publish to the Tanzu Platform marketplace as governed, context-rich services that both agents and developers can discover and use without data ever leaving the enterprise.

Private AI agents get a deny-by-default runtime from Broadcom

As AI agents take on enterprise tasks, companies face a new battle over access and control Enterprise private AI has moved past the pilot stage, and the shift is exposing an awkward gap. Agents that write code, process claims and run business workflows need models, tools and data to be useful, yet few organizations want autonomous software wandering across their infrastructure unsupervised. That tension is putting platform teams back at the center of enterprise architecture. The same cloud-native disciplines -- orchestration, telemetry, observability and role-based access control -- are now being applied to fleets of agents. Because agentic applications are essentially microservices applications, the platform is a natural place to enforce control, according to Purnima Padmanabhan (pictured), general manager of the Tanzu Division at Broadcom Inc. "Agents have, by definition, agency, which means you just give the intent and resources and then the agent interprets that intent and decides to do something," Padmanabhan said. "You have to say, 'OK, I want to be able to build agents fast, I want to be able to build that securely and I want to run them, but I want to run them in a sandboxed way.'" Padmanabhan spoke with theCUBE's John Furrier at VMware Explore 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed agent runtimes, trusted open source and the data foundations behind production AI. (* Disclosure below.) Private AI gets a deny-by-default agent runtime Broadcom's answer to the challenge of securing autonomous agents is a runtime rather than a toolkit. Tanzu Platform Agent Foundations is now an integral part of VMware Private AI Cloud, giving agents a sandboxed place to run where every model, tool and dataset must be explicitly bound, Padmanabhan explained. Curated data products sit alongside it, handling chunking, vectorization and access control so agents can work with curated data without directly accessing original sources. "The right way to secure an agent is to put it in a black box and give it nothing, but then you won't get any intelligence," Padmanabhan said. "I want to give it on my terms in a curated way with identity, with [role-based access control], with credential management. Being able to connect it to curated data sets all on a single platform means I can move faster." Trust in the underlying code matters just as much, since agents build with whatever libraries they are handed. Broadcom is scanning both its commercial repositories and open source with Mythos and expanding its Spring and Java security work into Python and Node.js, with June's patch release the largest in 23 years of Spring stewardship, Padmanabhan noted. "We are finding issues. We are finding vulnerabilities," she said. "These vulnerabilities are not just your low-level vulnerabilities, these are things that can completely bring down an enterprise. We take that job very seriously." For business leaders, the calculation is less about technology than timing, as coding agents deliver measurable gains and workflow automation follows close behind. Private AI, in that framing, is infrastructure for a competitive position rather than a science project. Organizations that move quickly can turn AI into a source of revenue and competitive advantage, while those that wait risk falling behind, Padmanabhan noted. "I think the message to the CIO is this is the chance to shine, right? Because this is a revenue opportunity for business," Padmanabhan said. "It's also a competitive advantage, and if you don't do it, it's a competitive disadvantage, because everybody else is going to be doing it." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of VMware Explore 2026: (* Disclosure: TheCUBE is a paid media partner for the VMware Explore 2026 event. Neither Broadcom, the sponsor of theCUBE's event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Cloud infrastructure becomes security front line for agentic AI

Agentic AI blows up the attack surface as security moves into the infrastructure layer The enterprise attack surface is expanding rapidly as agentic AI brings a constantly shifting cloud infrastructure into play. That pace is pushing security decisions down toward the virtualization layer, where policy can be enforced without slowing traffic. Perimeter defenses alone no longer hold, and enterprises that delay a broader lateral security program risk falling behind increasingly automated attacks, according to Umesh Mahajan (pictured), vice president and general manager of the Application Networking and Security Division at Broadcom Inc. "This is the time where you can't put off security any longer," Mahajan said. "'Oh, I got a perimeter firewall. I'm good.' No, no, no - not good. It can be bypassed. Now the security gurus or experts are saying, 'No, you can't take two years, three years. You have to deploy lateral security.'" Mahajan spoke with theCUBE's John Furrier at VMware Explore 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed agentic AI's effect on the enterprise attack surface, zero-trust enforcement and API protection for Kubernetes workloads. (* Disclosure below.) Building zero trust into cloud infrastructure Enterprises have often bought security tools piecemeal over the years, and the seams between them are where attackers operate. Broadcom's answer is an integrated software stack in which the elements share context, delivered through its vDefend and Avi Load Balancer product lines, Mahajan explained. "Our customers have bought multiple security products. They can't put it together," he said. "It's like buying Swiss cheese. Yeah, you have pieces of security, but you have plenty of holes which people can drive through." Scale is the other constraint, Mahajan noted. AI workloads generate heavy east-west traffic and punish any inspection step that adds delay, which is why the company has pushed enforcement into the hypervisor rather than a separate appliance tier, part of a wider update to VMware's security portfolio for AI-era threats. That includes firewalling and intrusion detection and prevention, with the company aiming to handle security processing at high throughput while keeping latency low. "We are doing 75 terabits per vCenter cluster for firewalling. We are doing 17 terabits for IDS IPS, and the other aspect is also latency," Mahajan said. "Because in AI workloads, latency matters, so our security is done at the hypervisor level." In other words, Broadcom is pushing security enforcement into the hypervisor to inspect traffic at scale without introducing the latency of sending it through separate security appliances. But protecting those workloads also requires visibility into what is running, Mahajan noted. Agents and Model Context Protocol services are transient, so administrators need a real-time picture of what is authorized and what is shadow IT before they can quarantine anything, and that visibility work is now landing alongside private cloud modernization programs. Bolting protection on later, once the cloud infrastructure is already carrying production AI traffic, is the failure mode executives are trying to avoid. "It has to be at the infrastructure level; it has to be at scale," he said. "Otherwise, when are you going to do it? Two years from now, by that time you'll be compromised." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of VMware Explore 2026: (* Disclosure: TheCUBE is a paid media partner for the VMware Explore 2026 event. Neither Broadcom, the sponsor of theCUBE's event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Broadcom Inc. Introduces AgentMinder, An Enterprise Solution For AI Agent Governance And Runtime Control

Broadcom Inc. introduced AgentMinder, a new solution that acts like a traffic controller for autonomous artificial intelligence (AI) agents. AgentMinder independently verifies agent identity and authorizes each action against its declared mission, intent, context, and current risk before the action reaches an enterprise resource. AgentMinder addresses these requirements by providing a consistent framework for managing AI agents across models, tools, and deployment environments. By establishing policy-based controls, continuous authorization, and full telemetry, AgentMinder helps enterprises scale agentic AI while maintaining security, compliance, and operational trust. AgentMinder enables enterprises to turn autonomous AI agents into governed digital employees via the following capabilities: Identity and intent: AgentMinder treats agents as enterprise-grade identities and binds their authority to a declared mission, permitted intents, approved tools, and authorized resources. Every agent must declare what it is trying to do, not just who it is, before it can touch enterprise systems. Runtime enforcement: AgentMinder?s cloud-native AI gateway secures every tool call at runtime, authenticating tokens and directing traffic exclusively to authorized backends. Driven by a dynamic policy engine, the solution instantly evaluates context?from user identity to intent?to help enable total policy compliance. Observability and audit: With an observability layer built on OpenTelemetry, AgentMinder provides security, risk, and platform teams full, compliance-grade visibility into every agent session and action, delivering chain of custody, anomaly detection, and operational insight at machine speed. This combination of intent governance, runtime enforcement, and deep auditability enables enterprises to safely scale agentic AI into high-risk workflows such as Finance, HR, or IT without rewriting their existing identity or authorization stack. With a cloud-native architecture, AgentMinder is deployed alongside existing large language models (LLMs)?whether on-premises, in virtual private clouds (VPCs) or across public cloud environments. By integrating with existing authorization stacks via the AuthZEN standard, organizations can reuse current policy enforcement endpoints without routing traffic through a single software as a service (SaaS) chokepoint. AgentMinder benefits enterprises by helping secure agents with real identities and auditable controls, making AI costs visible and predictable, and deploying wherever their models run?all in a single, integrated solution. AgentMinder provides a highly flexible deployment model across platforms like VMware vSphere Kubernetes Service (VKS), Google Cloud Platform, and other standards-based, cloud-native Kubernetes platforms. AgentMinder?s multi-region, active-active architecture supports continuous uptime, effortlessly supporting peak loads of nearly 36 million customer-related and seven million workforce-related API calls every single day. AgentMinder is generally available.

Cloud AI economics push enterprises toward private AI clouds

From private cloud to private AI cloud, software decides who wins Enterprise experimentation with cloud AI has run into a wall of data-control, sovereignty and token-cost questions, pushing intelligence back toward infrastructure enterprises own. The result is a rebuild of the private cloud as an AI platform, where production inference, not experimentation, sets the requirements. That shift lands squarely on information technology operations teams, which must serve frontier models, small local models and swarms of agents from the same pool of hardware. Mixing those workloads without driving up server, energy and licensing costs is now the central architectural problem, according to Chris Wolf (pictured), global head of AI and advanced services, VMware Cloud Foundation Division, at Broadcom Inc. "You have sovereignty considerations. You have tokenomics considerations as well. This doesn't mean don't use frontier models. It means be practical," Wolf said. "Use frontier models where it makes sense, where [you] need deep reasoning. Use specialized models, local SLMs, where they make sense as well. You're really seeing this breadth of coverage happening in the industry - and now IT operations is caught in the middle of all of this." Wolf spoke with theCUBE's John Furrier at VMware Explore 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed the move from private cloud to private AI cloud, AI factory operations, sovereignty and how enterprises should plan the next 18 months. (* Disclosure below.) Cloud AI economics put software ahead of hardware Agentic workloads have added new demands to that mix, including warm pools of isolated virtual machines that can spin up agents without allowing escapes or privilege escalations. Memory is the other constraint, spanning key-value cache placement across graphics processing units and tiering across storage classes, which is why Broadcom has been positioning VMware Cloud Foundation as the pooling layer. But too many buyers still start at the wrong end of the stack, Wolf said. "'Buy your hardware first, figure out the software later' - no, that's a horrible idea, because you have to make sure that your software choices are compatible with the hardware you bought," he said. "Software is what's giving you the ability to have autonomy in terms of the accelerators you use, to have the flexibility to ensure that I can use cloud models when I need to [or] use local models when I need to." That gap is what Broadcom is targeting with its AI factory approach, which provisions from bare metal through model runtimes and then exports a YAML file to clone additional clusters. Customers arriving from earlier deployments tend to describe the same experience, Wolf noted. "People were running into buyer's remorse," he said. "They bought what they thought was this full turnkey solution, and as it turns out, it wasn't." Sovereignty has become the other driver, with governments in North America, Europe and Asia demanding localized models, data planes, encryption keys and control planes. For enterprises weighing cloud AI against on-premises builds, the idea is to slow down before committing, Wolf explained. "More than ever, they have to architect for the expectation of change. They can't architect based on what looks good today, because the space is moving too fast," he said. "Make sure software is at the forefront of your architecture and decision-making, and then go from there." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of VMware Explore 2026: (* Disclosure: TheCUBE is a paid media partner for the VMware Explore 2026 event. Neither Broadcom, the sponsor of theCUBE's event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Broadcom Inc. Delivers End-to-End Security, Identity, and Observability for Agentic AI

Broadcom Inc. announced comprehensive security, identity, and observability capabilities for agentic AI environments running on Private AI Cloud. With AgentMinder, VMware vDefend, and VMware Avi Load Balancer, Broadcom provides advanced, multi-layer cybersecurity solutions designed to govern autonomous AI agents, defend agentic workloads, and protect enterprise Private AI Cloud workloads. Private AI Cloud delivers a more secure and cost-effective AI infrastructure to run inference workloads and agentic AI. It features the advanced security and automated compliance guardrails to help secure the workloads while helping to prevent data exfiltration and reducing cyber risk across every AI workload. Customers benefit from an agentic AI pipeline to build, run and govern trusted agents to innovate rapidly. The enterprise attack surface expands dramatically across agents, tools, datastores, and large language models (LLMs) as organizations deploy agentic AI applications using new protocols such as Model Context Protocol (MCP) and agent-to-agent (A2A) communications. Broadcom?s advanced cybersecurity solutions address these emerging blind spots by establishing trusted agent identities, enforcing Zero Trust boundaries, and providing enhanced visibility and granular control across the entire AI ecosystem. Comprehensive Defense and Governance for Agentic AI: These solutions address critical agentic AI security and operational risks across three core layers: VMware vDefend, VMware Avi Load Balancer and Broadcom AgentMinder. VMware vDefend: Agentic Zero Trust: VMware vDefend delivers Zero Trust lateral security for AI workloads running on VMware Cloud Foundation. The following new vDefend enhancements will extend its ZeroTrust lateral security to agentic AI workloads: Discovery of Agentic AI Components: Will automatically identify MCP servers, LLMs, datastores, and tools by continuously monitoring traffic flows across VMware Cloud Foundation. Shadow AI Monitoring: Will detect unauthorized AI usage and enforce strict Zero Trust policies to help maintain complete control over enterprise environments.AI-Generated IDPS Signatures: Will use an agentic AI pipeline to create Intrusion Detection and Prevention (IDPS) signatures at machine scale and speed, providing distributed virtual patching to protect workloads against the volume and velocity of AI-discovered vulnerabilities. VMware Avi Load Balancer: Agentic Threat Defense: Avi Load Balancer, with its seamless integration with Kubernetes including VMware vSphere Kubernetes Service (VKS), multi-terabit performance, elastic scale-out operation is ideally suited to deliver AI-aware load balancing, web application security and API protection (WAAP) to agentic AI workloads. The following new enhancements will broaden its protection of agentic AI workloads: Tool and Agent Misuse Prevention: Will help restrict agents from accessing unauthorized MCP tools and inspect transaction content to block malicious execution, including remote code execution (RCE) and file injection. Zero Day Attack Detection: Will establish normal agentic traffic baselines across agents, LLMs, and tools to flag and isolate anomalous behavior in real time. Sensitive Data Protection: Will help prevent unauthorized exfiltration of credentials, personally identifiable information (PII), and sensitive financial data. Broadcom AgentMinder: Agent Identity, Intent & Governance: Unveiled today, AgentMinder serves as the central control plane for autonomous AI agents (read the press release). As agents transition from content generation to active business process execution, AgentMinder delivers the following core governance capabilities: Identity and Intent Controls: Treats autonomous agents as enterprise-grade identities and binds their authority to a declared mission, permitted intents, approved tools, and authorized resources. Runtime Policy Enforcement: Features a cloud-native gateway that evaluates real-time context (identity, tool, intent, resource) and enforces least-privileged policies on every tool invocation. Compliance-Grade Auditability: Built on OpenTelemetry to deliver compliance-grade visibility into every agent session and action, delivering chain of custody, anomaly detection, and operational insight at machine speed.

Broadcom Inc. Unveils AI-Ready Data Foundations in VMware Tanzu Platform to Power Secure Enterprise AI Cloud

Broadcom Inc. announced new AI-ready data foundations for the VMware Tanzu Platform, the official agent platform for VMware Private AI Cloud. The update delivers a complete, end-to-end framework enabling enterprises to transition safely from initial AI pilots to fully production-ready AI agents inside their own secure private clouds. As organizations adopt agentic AI, business leaders face critical trust and security hurdles. Unlike traditional software, AI agents act autonomously and query data independently, creating significant risks around data leakage, unexpected cloud egress fees, and inaccurate outputs caused by uncurated information. The latest VMware Tanzu Platform release solves this agent trust problem in two ways: securing the agent and securing the data. Agents run in hardened sandboxes leveraging services from a curated marketplace, so every agent operates with isolated credentials and explicit connections to the services it uses. Data is governed for access, context, and lineage. Access means agents reach the right data and only the right data. Context means data is prepared efficiently before agents consume it, improving accuracy and lowering token costs. Lineage means every agentic decision can be traced to exactly what data was used and where it came from. Key capabilities introduced in the VMware Tanzu Platform include: Hardened Agent Sandboxes: Enforces a "deny-by-default" security containment model that completely isolates credentials, helping to prevent prompt injection attacks and unauthorized network access.AI-Ready Data Foundations: Processes structured and unstructured enterprise data on-site, delivering high-precision context to AI agents to improve accuracy, reduce hallucinations, and lower token costs .Out-of-the-Box Developer Harness: Accelerates build times with pre-approved skills, step-by-step workflow buildpacks, human-in-the-loop controls, and integrated memory services. Curated Marketplace: Operates a centralized catalog where developers and agents can safely discover and connect to vetted AI models, tools, and data products. Auditable Agent Governance: Integrates an AI gateway to monitor, rate-limit, and log every action an agent takes for strict compliance and auditing. Availability: Capabilities announced here will be generally available in Tanzu Platform in Fall 2026.

Product marketing shifts as private cloud powers production AI

Production AI shouldn't need another stack. But can private cloud deliver? Private AI is moving into production, and enterprises are demanding more than another collection of AI components. Product marketing is now about turning that complexity into a turnkey system that can get AI running faster. The bet is that the platform enterprises already use to run virtual machines and containers can also run their models and agents, delivered as a single turnkey system rather than a stack customers assemble themselves, according to Prashanth Shenoy (pictured), chief marketing officer and vice president of the VMware Cloud Foundation Division at Broadcom Inc. "We have reached a very critical juncture in the world of AI," Shenoy said. "A lot of our organizations are moving from pilot to production at scale, so there are big concerns around cost, tokenomics, security and privacy concerns of their data. A lot of our organizations are looking towards private cloud as the preferred platform for deploying their production AI workloads." Shenoy spoke with theCUBE's John Furrier at VMware Explore 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed the VCF AI Factory, private AI services and frontier AI security. (* Disclosure below.) Product marketing moves toward turnkey AI factories Without a packaged offering, customers stitch together virtualization, storage, Kubernetes services and a model gallery on their own, which creates silos and slows deployment. Broadcom has certified servers from multiple vendors, worked with Advanced Micro Devices Inc. and Nvidia Corp. on the chip layer and has partnered with MetalSoft Cloud Inc. on heterogeneous firmware and hardware bring-up, Shenoy explained. The goal is to bring those components together into a single operational layer rather than leave customers to integrate them themselves. "All of this is integrated into the VCF Ops Console," he said. "The operations become easy, and the provisioning of this hardware and management gets reduced from months [or] weeks, to now minutes." Model choice matters as much as hardware certification. About 150 models have been tested and optimized to run on VCF, spanning open-source, open-weight and commercial options, alongside an AI gateway that connects local models to more than 40 cloud model providers, Shenoy noted. "Not every use case that enterprises have requires a frontier LLM model," he said. "It's all about purpose, fit, governance and cost, which is very, very critical." Security is the other half of the story, with frontier AI a central theme at this year's VMware Explore event. Broadcom hardened VCF 9.1 from the ground up, moved to monthly patch releases and is arming practitioners with new certifications. All this is in service of Broadcom's Frontier AI Security Readiness Program, which covers assessment, architecture, implementation and upskilling, Shenoy said. The program is designed to help organizations prepare for threats that are moving at AI speed. "The volume, the velocity and the type of variety of these AI-driven threats have just exploded," he said. "Attackers don't take weeks or months. They take hours or minutes to get into the system." Years of product marketing decisions to collapse the VMware portfolio into fewer stock-keeping units now look like preparation for this moment, with capabilities such as memory tiering built in before the server supply crunch arrived. The payoff is that AI does not require a parallel estate, Shenoy noted. "You don't need to create another siloed infrastructure," he said. "The same infrastructure that you've tried and tested for running your VMs - for running your containers - can be used to run your agents and AI workloads, with the same unified operations management and the security and data privacy." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of VMware Explore 2026: (* Disclosure: TheCUBE is a paid media partner for the VMware Explore 2026 event. Neither Broadcom, the sponsor of theCUBE's event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Broadcom Inc. Announces Validation of Leading AI Models Including Nemotron 3, Gemma 4, Cotomi, Qwen 3.7-Max, and GLM 5.2 on VMware Cloud Foundation

Broadcom Inc. announced that leading AI models from providers including Google, NVIDIA, NEC, Alibaba Cloud, and Z.ai, are validated to run on VMware Cloud Foundation (VCF), enabling customers to bring these AI models on-premises and deliver ?model as a service? to their users. VCF provides the AI- and Kubernetes-native platform that gives organizations across industries a production-ready path to deploying a wide variety of AI models. VCF is a unified private cloud platform capable of running inference workloads, agentic applications, containerized services, and traditional VMs together, eliminating the operational fragmentation of managing separate stacks. Independent benchmark testing under MLPerf Inference v5.1 standards confirms that VCF delivers performance on par with bare metal, making it the ideal platform of choice for enterprises deploying AI at scale on premises. VCF empowers enterprises to accelerate AI workload deployment at lower costs through an open and extensible ecosystem. Support for mixed compute across AMD, Intel, and NVIDIA frees enterprises to choose their preferred GPU and CPU hardware for their AI workloads. Leveraging vLLM as the default model runtime gives customers the ability to run more than 150 open source models on VCF. Broadcom is announcing the following models have been tested and validated to run on VCF: Nemotron 3: The NVIDIA Nemotron 3 family of open, multimodal models delivers leading accuracy and efficiency to help agents complete tasks faster. Combining hybrid Mamba-Transformer MoE architecture, 1 million context and multi-environment reinforcement learning, Nemotron 3 enables scalable, long-running agentic workflows across enterprise applications. Gemma 4: Google DeepMind's latest open source, open-weight multimodal model family, purpose-built for developers and the research community for bringing local execution, and enabling enterprises to build and deploy autonomous AI agents. cotomi: NEC's proprietary AI model optimized for Japanese language, trained on curated, highly reliable datasets. It empowers enterprises by seamlessly combining high-speed processing with a 40% improvement in token efficiency. Qwen 3.7-Max: Alibaba's Qwen 3.7-Max is a proprietary multimodal model that offers impressive one-million-token context windows, advanced multimodal reasoning, and agentic-era design, giving global enterprises sovereign, on-premises access to one of the world's most capable AI model families. GLM 5.2: Z.ai (formerly Zhipu AI)'s open source General Language Model enables enterprises to deploy coding and reasoning agents locally for multi-step autonomous workflows with data sovereignty and optimal hardware performance.

Private AI cloud shift pulls AI workloads back on-premises

Enterprises pull AI workloads back on-premises as costs and threats mount Enterprise infrastructure strategy is being rewritten around the private AI cloud, as security exposure, spiking hardware prices and rising token costs push artificial intelligence workloads back inside the data center. What started as a virtualization refresh has become the main event for on-premises computing. That shift has put platform consolidation at the center of enterprise buying decisions, a theme running through this year's VMware Explore. Broadcom Inc. has spent three years folding VMware's accumulated products into a single stack, and customers ranging from small businesses to United Airlines, Audi and the London Stock Exchange are now running on it, according to Krish Prasad (pictured), senior vice president and general manager of the VMware Cloud Foundation Division at Broadcom. "We have taken the goodness that people see in the public cloud, which is the developer experience, the agility, and we have combined that with the things people like in the private infrastructure, which is security, which is cost controls, the resiliency," Prasad told theCUBE. "The combination is what VCF is all about, and that's why customers are very interested in deploying it." Prasad spoke with theCUBE's John Furrier at VMware Explore 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed platform consolidation, memory economics, sovereignty and the security demands of frontier models. (* Disclosure below.) Security and cost pressures redefine the private AI cloud Three forces are converging on enterprise infrastructure teams at once. Frontier models have raised the stakes on hardening, server prices driven by dynamic random-access memory are climbing, and companies want to keep intellectual property away from external models, Prasad said. "Customers are concerned about the token cost in the cloud. They are also concerned about IP protection, going to these external models and exposing their IP and data," he said. "So they are bringing the AI workloads back on-prem where they can keep it closer to their data and protect their IP." Security is the first line of defense in that move, and Broadcom has been updating VMware's platform for AI-era threats. The company had early access to frontier models through the Project Glasswing and turned that head start inward, Prasad noted. "We really have built Mythos-like frontier models into our software development lifecycle, so our infrastructure is pretty hardened by the time customers get it," he said. "We have done some innovations in our core platform, things like live patching ... where customers can patch their environment while the workload is running without disrupting the workload." Cost is the second lever. Memory tiering, shipped a year before the DRAM squeeze, cuts application memory requirements by about half by tiering to NVMe storage, while VCF 9.1 extends the AI stack across more than 150 models, AMD and Nvidia Corp. accelerators and any original equipment manufacturer server. For Prasad, that convergence defines the company's next private AI cloud bet. "The whole focus now is around making VCF the best place for running the workloads. That's where our customers are focused," he said. "Everything that goes around AI, the security, the runtime, the models, and all of that. So that's the big bet we are making, and we are doubling down on it." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of VMware Explore 2026: (* Disclosure: TheCUBE is a paid media partner for the VMware Explore 2026 event. Neither Broadcom, the sponsor of theCUBE's event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Broadcom Inc. Introduces VMware Private AI Cloud, Enabling Enterprises to Scale AI Cost-Effectively, Operate More Securely, and Innovate Rapidly

Broadcom Inc. introduced VMware Private AI Cloud, a more secure, scalable, and flexible approach to AI that brings the model to the data, not the data to the model. Built on Broadcom's advanced software capabilities, VMware Private AI Cloud gives organizations a production-ready path to securely building, running, and governing inference workloads, agentic applications, and traditional enterprise workloads together on a single private cloud platform. Offering diverse hardware, model, and accelerator choices, VMware Private AI Cloud lets enterprises scale AI cost-effectively, operate more securely, and innovate rapidly. Scale AI Cost-Effectively: VMware Private AI Cloud addresses the three core AI cost drivers: hardware CapEx, operational complexity, and token economics (tokenomics). VMware Cloud Foundation (VCF) 9 lowers hardware costs via NVMe memory tiering and cluster-wide storage deduplication. VCF supports GPUs, CPUs, and accelerators from leading vendors, along with server hardware from major OEM and ODM vendors, allowing customers to run heterogeneous clusters cost-effectively. To optimize tokenomics and resource usage, it features token monitoring, multi-tenant model sharing, enhanced GPU/vGPU tracking, and an AI metrics observability dashboard. Infrastructure and operations innovations announced for Private AI Cloud include: Metal to model faster with VMware AI Factory: Broadcom announced VMware AI Factory, the software-defined foundation of VMware Private AI Cloud, providing customers a simplified path to production with new automation innovations for deploying AI-ready infrastructure and Day 2 operations. With VMware AI Factory, customers can achieve faster time to first model deployment and better manage AI tokenomics. Validated AI Models for VCF enable Model as a Service: VMware AI Factory gives enterprises a production-ready path to running leading AI models on-premises. VCF customers can run more than 150 open source and commercial models, including Nemotron 3, Gemma 4, cotomi, Qwen 3.7-Max, and GLM 5.2. Broadcom is working with the world's leading AI model providers to give organizations a clear path to data sovereignty and cost-effective AI at scale, with leading models available securely and delivered as a service to their user community through VCF's built-in services.Operate More Securely: Designed with a defense-in-depth approach aligned to NIST CSF 2.0, VCF protects against AI-accelerated threats by minimizing the attack surface and enabling continuous compliance. Automated, non-disruptive updates keep systems current, while VMware vDefend uses virtual patching and hypervisor-level lateral security with microsegmentation to enforce Zero Trust and block exploits. Furthermore, vDefend?s multi-layer threat defense and VMware Avi Load Balancer?s web application firewall and API protection prevent sophisticated attacks. Security innovations supporting Private AI Cloud include:TrueSource by Broadcom for verifiably built open source: Designed to address the acceleration in AI-enabled exploitation, Spring Enterprise delivers secure, curated Spring releases from the team that maintains it, including frontier model scanned, human-verified patches delivered simultaneously across every release line. TrueSource Trusted Artifacts extends clean-room builds across the Java ecosystem, Python, and Node.js, along with the Bitnami Secure Images catalog while TrueSource Data Services brings the same standard to the data tier: PostgreSQL, RabbitMQ, MySQL, and Valkey. Agentic Zero Trust with vDefend: New vDefend enhancements will extend Zero Trust lateral security for agentic AI workloads by identifying agentic AI components through continuous monitoring of traffic flows, detecting unauthorized usage of shadow AI, and providing distributed virtual patching through AI-generated Intrusion Detection and Prevention (IDPS) signatures. Agentic Threat Defense with Avi Load Balancer: Avi web security will broaden its protection of agentic AI workloads by restricting agents from accessing unauthorized tools and preventing misuse; flagging and isolating anomalous behavior to detect zero day attacks; and helping to prevent the unauthorized exfiltration of sensitive data with data protection guardrails. Innovate Rapidly for the Agentic AI Era: Unlike traditional apps, autonomous AI agents can act unchecked, exceed scope, or misinterpret instructions. Consequently, trust depends on robust controls and data integrity. VMware Tanzu Platform, with VMware vDefend, provides a foundation for trustworthy enterprise agents via a deny-by-default architecture, a prebuilt harness, and a curated marketplace. Agentic AI innovations announced for Private AI Cloud include: AgentMinder by Broadcom helps govern autonomous AI agents at scale: Broadcom today unveiled AgentMinder, a new solution that provides enterprises with a central control plane for autonomous AI agents. It treats agents as enterprise-grade identities, binding their authority to a specific mission, approved tools, and authorized resources. Additionally, it provides runtime policy enforcement?ensuring least-privileged access for every tool invocation?and delivers compliance-grade auditability, giving enterprises full visibility into what their agents are doing. AI-ready data foundations turn enterprise data into AI agent knowledge: New AI-ready data foundations in Tanzu Platform let data owners build and manage dynamic pipelines across structured and unstructured data, producing data products optimized for low-cost agent consumption. Those products publish to the Tanzu Platform marketplace as governed, context-rich services that both agents and developers can discover and use without data ever leaving the enterprise. Tanzu Platform agent foundations to build and run trusted AI agents: Tanzu Platform agent foundations enforce strict containment through a deny-by-default runtime; agents have zero access to APIs, networks, MCP servers, or the internet unless explicitly granted. New enhancements include an isolated credential store that shields all credentials from agents entirely. Agents can't leak or misuse what they can't see, closing off credential theft and prompt injection attacks.

Broadcom's VMware Private AI Cloud spans infrastructure, agents, data and security

Broadcom's VMware Private AI Cloud spans infrastructure, agents, data and security Broadcom Inc. today introduced VMware Private AI Cloud, an integrated software stack designed to enable enterprises to build and run artificial intelligence applications alongside conventional workloads while keeping their data, models and infrastructure under their control. Announced at the VMware Explore 2026 conference this week in Las Vegas, the offering combines VMware Cloud Foundation infrastructure with a new VMware AI Factory, validated AI models, VMware Tanzu agent-development and data services, AgentMinder governance software and expanded security protections. Broadcom also unveiled TrueSource, a portfolio of commercially supported and verifiably built open-source software. "It brings the private cloud infrastructure with the private AI services into one, enabling production inference and agentic AI with the data sovereignty, the compliance, and the cost predictability required for our customers," said Prashanth Shenoy, chief marketing officer and vice president of marketing for VMware's cloud platform, infrastructure and solutions organization. The foundation is VCF 9, which supports virtual machines, containers, inference workloads and agentic applications on one platform. Broadcom said VCF 9 has more than 3,000 customer deployments representing over 19 million allocated processor cores. Its nonvolatile memory express memory-tiering technology can replace lower-cost flash storage for some dynamic random-access memory and has reduced per-host costs by up to 42% in customer deployments while maintaining performance, Shenoy said. Cluster-wide storage deduplication is intended to further reduce capacity requirements, while token monitoring, graphics processing unit tracking and an AI metrics dashboard provide operators with visibility into resource consumption. VMware AI Factory packages VCF with hardware, accelerators, AI software and models that Broadcom and its partners have tested together. Initial configurations include Advanced Micro Devices Inc.'s Instinct MI350-series graphics processors and servers from Cisco Systems Inc., Lenovo Group Ltd. and Super Micro Computer Inc. Integration with MetalSoft Cloud Inc.'s orchestration platform automates provisioning and lifecycle management across heterogeneous bare-metal systems through the VCF operations console. The platform supports accelerators from AMD, Intel Corp. and Nvidia Corp. and uses vLLM as its default model for inference and LLM serving. Broadcom said customers can run more than 150 open-source and commercial models. Newly validated models include Nvidia's Corp.'s Nemotron 3, Google LLC DeepMind's Gemma 4, NEC Corp.'s Japanese-language cotomi, Alibaba Group Holding Ltd.'s Qwen 3.7-Max and GLM 5.2 from Jingsheng Hengxing Technology Pte. Ltd., better known as z.ai. "Not every AI use case requires a frontier AI model and a large language model to be deployed," Shenoy said. Model choice increasingly depends on the job, cost and governance requirements. New multitenant model sharing will let an organization deploy a model once and make it available to multiple business groups while isolating each tenant's data. Broadcom is designating Tanzu Platform as the agent layer of VMware Private AI Cloud. The Cloud Foundry-based platform-as-a-service that serves a pre-engineered AI application development platform will provide deny-by-default sandboxes. Agents cannot access application programming interfaces, networks, Model Context Protocol servers or the internet unless permission is explicitly granted. Credentials are kept in a separate store so agents can't view or disclose them. An out-of-the-box development harness will include approved skills, workflow buildpacks, human-review controls, and memory services. A curated marketplace will provide access to vetted models, tools, skills and data products, while an AI gateway will monitor, rate-limit and log agent actions. The new AI-ready data foundations also address the other side of agent governance: controlling the information agents use. Tanzu will ingest and parse structured, unstructured and multimodal information inside the customer's environment, add metadata and a semantic layer and turn it into governed data products. Those can then be published in the Tanzu marketplace with role-based access controls and lineage information. "You simply give access to that curated data product that is consistently kept in sync," said Purnima Padmanabhan, vice president and general manager of Broadcom's Tanzu Division. The Tanzu capabilities are scheduled to become generally available in fall 2026. Agentic fabric AgentMinder, which is generally available immediately, provides an additional control plane independent of the agent runtime. It assigns agents identities and evaluates each attempted action against the agent's owner, declared mission, intent, approved tools and authorized resources. A gateway can allow, deny, redirect or redact requests before they reach a model, MCP server, API or other enterprise resource. "AgentMinder is what we call an agentic fabric," said Clayton Donley, vice president and general manager of Broadcom's Identity Management Security Division. "It's sitting between the agents you have and the LLMs and PCs, MCP services, APIs and other kinds of tooling that those agents are going to be calling." Every time an agent makes a request AgentMinder verifies its identity and enforces least-privilege policies using certificate-based machine identities. It also integrates with existing authorization systems through the AuthZEN standard. Its OpenTelemetry-based observability layer records prompts, tool calls and policy decisions to create an audit trail. Broadcom said it uses the technology internally to handle nearly 36 million customer-related and seven million workforce-related API calls daily. Planned VMware vDefend enhancements will discover agents, models, MCP servers, datastores and tools by inspecting VCF traffic. The software will also identify unauthorized "shadow AI" activity and use an agentic pipeline to generate intrusion detection and prevention signatures. Forthcoming Avi Load Balancer features will inspect agent transactions for tool misuse, remote code execution, file injection and sensitive data exfiltration. "Security is the middle name in agentic AI," said Umesh Mahajan, vice president and general manager of Broadcom's Application Networking and Security Division. TrueSource extends the security strategy to software supply chains. It combines Spring Enterprise, TrueSource Trusted Artifacts and TrueSource Data Services. Trusted Artifacts adds clean-room builds for the broader Java, Python and Node.js ecosystems and hardened Bitnami container images. Data Services covers PostgreSQL, RabbitMQ, MySQL and Valkey. Broadcom uses frontier models to scan Spring and more than 5,000 libraries in its dependency tree, but engineers review the fixes and contribute them upstream. "We are remediating not around maintainers, but with the maintainers," Padmanabhan said. All three TrueSource offerings are available under tiered site licenses. Pricing was not disclosed.

Broadcom Inc. Announces VMware AI Factory as Software-Defined Foundation of VMware Private AI Cloud

Broadcom Inc. announced VMware AI Factory, the software-defined foundation of VMware Private AI Cloud. VMware AI Factory provides customers a simplified path to production AI with new automation innovations for deploying AI-ready infrastructure and supporting Day 2 operations. VMware AI Factory brings AI applications directly to enterprise private data within a secure private cloud environment. VCF?s unique infrastructure automation capabilities can reduce the time from bare metal server deployment to serving the first AI model from weeks to a matter of hours. VMware AI Factory streamlines AI infrastructure management by fully automating hardware provisioning, software stack enablement, and end-to-end lifecycle management. By integrating hardware and software operations into a unified, automated solution, organizations can rapidly scale AI workloads while minimizing operational complexity. As part of the VMware AI Factory, private AI services help make AI operational, governable, and cost-effective. VCF pools and shares GPU resources across the organization so teams can run multiple models on shared hardware instead of dedicating infrastructure to each workload. A unified model gallery gives IT and data science teams a single interface for deploying and managing model inference and RAG workflows across VMs, containers, and GPU resources, with built-in observability into token throughput, latency, and compute and memory utilization. Enterprises can pivot to new models while keeping costs low through shared infrastructure and governed models-as-a-service. New and forthcoming private AI services include multi-tenant model sharing, AI Gateway for unified model governance between on-premises and cloud environments, and secure AI sandboxes and governance. Broadcom is announcing a new partnership with MetalSoft to deliver integrated heterogeneous bare metal automation for VCF that drops bare-metal provisioning time from weeks to minutes. The integration will help IT provision or repave physical servers from multiple vendors directly through the VCF management console, unifying the software and hardware lifecycle into a single operational model and eliminating the need for vendor-specific tools for hardware and firmware management. VMware AI Factory combines VMware Cloud Foundation (VCF) with certified VCF AI ReadyNodes from Cisco, Dell Technologies, Lenovo, Supermicro and others and customers? preferred AI software and accelerator architectures. Broadcom and AMD are collaborating to deliver a VMware AI Factory that pairs VCF with AMD Instinct GPUs and the open AMD ROCm software ecosystem. Zero-touch provisioning will orchestrate the end-to-end deployment of the entire stack, from vSphere and vSAN through Kubernetes and the AMD GPU operator, and the AMD DVX driver can attach GPUs to large VMs consumed by a VMware vSphere Kubernetes Service cluster. VMware AI Factory gives enterprises a production-ready path to running leading AI models on-premises. VCF customers can run more than 150 open source and commercial models, including Nemotron 3, Gemma 4, cotomi, Qwen 3.7-Max, and GLM 5.2. Broadcom is working with the world?s leading AI model providers to give organizations a clear path to data sovereignty and cost-effective AI at scale, with leading models available securely and delivered as a service to their user community through VCF?s built-in services.