VAST Data - Experts & Thought Leaders

VAST Data, the AI Operating System company, announces a new Agent Sandbox Foundation Stack with NVIDIA, built with NVIDIA NemoClaw and NVIDIA OpenShell to provide the scalable, persistent data infrastructure enterprises need to securely run thousands of AI agents and sub-agents in production. The stack helps enterprises and AI service providers deploy, operate and scale governed agent environments without tying persistent state to ephemeral compute. As enterprises deploy increasingly autonomous AI agents, infrastructure requirements change fundamentally. Agents can spawn other agents, execute tasks and disappear, with fleets creating and destroying thousands of isolated workspaces an hour. Autonomous AI agents Long-running agents may also work across multiple sessions, sleeping, resuming and delegating tasks to short-lived subagents along the way. Whether an agent runs for minutes or across multiple sessions, its progress, working data and memory need to persist independently of any individual sandbox or compute node – remaining available, governed and auditable wherever execution resumes. The VAST Foundation Stack for Governed Agent Sandboxes addresses this challenge by combining NVIDIA’s secure agent runtime technologies with the persistent infrastructure and data services of the VAST AI Operating System. NVIDIA OpenShell provides isolated execution and policy enforcement around agent activity, while VAST provides persistent state, scalable agent workspaces, integrated data services and operational records across the agent lifecycle. Individual compute nodes By decoupling persistent agent state from individual compute nodes, the architecture allows agents to pause, resume or move across eligible infrastructure without losing their progress, working data or context. Together, these capabilities let agent compute stay ephemeral while agent state stays durable. “Every enterprise that puts an agent into production will eventually need to answer what it did and why it did it,” said Alon Horev, CTO and Co-Founder at VAST Data. “An agent might exist for minutes, spawn thousands of other agents and then disappear, but its work has to persist and its actions have to stay understandable long afterward. Together with NVIDIA, we’re showing how the VAST AI Operating System provides the durable foundation underneath secure agent environments, so organizations can scale agentic systems without sacrificing persistence, governance or control.” Providing repeatable implementations “As AI agents take on more complex work, enterprises need infrastructure that can support secure execution while preserving the data, context and state agents depend on across their lifecycle,” said Kari Briski, Vice President of Generative AI Software at NVIDIA. “By combining NVIDIA NemoClaw and OpenShell with the VAST AI Operating System, organizations can build agent sandbox environments that are secure and designed to scale from experimentation to production.” The new Agent Sandbox Foundation Stack extends VAST’s work with NVIDIA to provide repeatable implementations for production AI. Rather than requiring organizations to assemble separate infrastructure for agent execution, data access, persistence and observability, the architecture brings these capabilities together across three layers: Infrastructure Built for Dynamic Agent Environments: The VAST AI OS provides persistent infrastructure underneath NVIDIA NemoClaw and OpenShell environments, supporting the rapid creation and removal of isolated agent workspaces. As new agent tasks are initiated, VAST uses zero-copy clones to rapidly provision persistent storage for isolated OpenShell sandboxes without creating full copies of the underlying data. This architecture is built for performance – designed to operate at the speed of agents by supporting efficient and highly dynamic workflows in which agents can spawn, disappear and reappear as reasoning demands change. Persistent State and Integrated Agent Data Services: Agent compute may be temporary, but its work does not have to be. VAST separates agent execution from persistent state so agents can pause, resume or transfer work across agent environments without starting from scratch. VAST DataStore provides persistent sandbox storage and snapshots, while VAST DataBase and integrated data services provide shared access to agent memory, context and enterprise data – allowing isolated agents to work concurrently from common data without creating redundant copies. Real-Time Observability and Durable Auditability: As agents take more independent actions, platform teams need visibility into both what is happening now and what happened after an agent disappears. The VAST Management System provides real-time visibility into data flows and infrastructure activity across agent fleets, while VAST DataBase maintains a persistent record of agent activity, including tool calls, prompts, tokens, costs and decisions. Together, these capabilities give teams operational visibility into active agent environments and a durable audit trail across increasingly complex multi-agent workflows. Changing compute environments VAST introduced Foundation Stacks to help organizations turn NVIDIA AI Blueprints into repeatable, production-ready AI implementations on the VAST AI OS. Initial Foundation Stacks showcased the use of VAST DataEngine for enterprise research assistance with NVIDIA Nemotron and video intelligence using NVIDIA Cosmos reasoning models and NVIDIA Video Search and Summarisation. The Agent Sandbox Foundation Stack extends that model into the infrastructure required to operate autonomous agents themselves in secure, governed sandbox environments. At NVIDIA GTC Berlin, VAST will demonstrate the Agent Sandbox Foundation Stack through a governed multi-agent workflow running NVIDIA NemoClaw and OpenShell on the VAST AI OS, showing agents dynamically spawning, accessing persistent data services and maintaining state across changing compute environments. VAST also plans to make the automated deployment available through a public GitHub repository, providing a reproducible path for organizations to deploy the architecture in their own environments.

VAST Data, the AI Operating System company, announces VAST DataEnclave, the confidential AI capability of the VAST DataEngine, built on NVIDIA Confidential Computing, with the support of ecosystem partners including top AI model builders, AI clouds, AI security and AI hardware providers. By enabling deployment inside customer data centres or trusted cloud hardware, including environments where leading AI models could not previously operate, VAST Data, in collaboration with NVIDIA and partners, is bringing proprietary and open AI models across a range of modalities to the world’s most sensitive data – while giving customers control over cost, performance, model selection and data privacy, and giving model builders reach into environments they could never serve before. Highly regulated industries Across financial services, healthcare, government and other highly regulated industries, some of the world’s most valuable data remains inside tightly controlled environments where moving it to an external AI service is impractical or prohibited. That creates a fundamental challenge for AI: sensitive data often cannot move to where leading models run, while model builders cannot distribute proprietary models into infrastructure they do not trust. VAST DataEnclave extends the VAST AI Operating System to resolve this impasse with a hardware-isolated secure runtime and cryptographic attestation that verifies the environment and its enforced policy before sensitive assets, such as proprietary models and sensitive data, are decrypted and loaded into the secure enclave container for analysis, where they remain protected in CPU and GPU memory during processing. Customer data keys remain under customer control, model keys and weights remain within the model builder’s trust domain, and infrastructure operators and administrators cannot access either while they are being processed. Making secure management Looking ahead, today's announcement expands the range of advanced models organizations can deploy within their own environments, while advancing VAST's broader AI Operating System vision, in which models are managed as a logical resource alongside data rather than as applications that simply sit on top of the infrastructure. As organizations adopt ecosystems of specialized models, each fit for a different purpose, priced differently and subject to different levels of trust, the AI OS will increasingly need to pair the right model with the right task and govern those models across environments: determining where they run, what data they can access, who or what can use them and the policies under which they operate. As organizations increasingly fine-tune their own models and agents generate specialized intelligence from their interactions, model weights become a new class of enterprise intellectual property, making secure management at scale an increasingly important function of the operating system. Fine-tuned models "Models are becoming a resource the operating system has to manage, the same way it manages data," said Renen Hallak, Founder & CEO of VAST Data. "That means knowing which model fits which task, what it can see, who can use it and under what rules, and doing all of that inside the same security and operational boundaries an enterprise applies to everything else. Bringing leading AI models securely to the world's most sensitive data is where this starts. Where it leads is a world where every organization is managing an ecosystem of fine-tuned models that represent its true intellectual property. The VAST AI Operating System is what keeps them secure, governed and useful." Conventional encryption protects model weights while they are stored and while they move across the network. Confidential computing extends encryption and protects data during execution. NVIDIA Confidential Computing, now in its third generation on Hopper, Blackwell, and Rubin platforms, ensures that sensitive data and models are only released during execution after the workload is verified and a secure enclave has been established. Trusted execution environments VAST DataEnclave uses NVIDIA Confidential Computing to create a secure container runtime and attestation service directly within the VAST DataEngine. Proprietary models execute inside secure enclaves established through CPU and GPU trusted execution environments. Key capabilities include: Hardware-Isolated Execution: Protects workloads inside confidential virtual machines and containers, using NVIDIA Confidential Computing to encrypt guest memory, GPU memory and NVLink traffic while isolating active data and models from infrastructure operators, administrators and other tenants sharing the same hardware. Verify-Before-Decrypt Attestation: Cryptographically verifies the trusted execution environment – including NVIDIA GPU attestation – before releasing decryption keys, ensuring sensitive assets are accessible only to approved workloads running in a trusted environment. Independent Key Control: Enables enterprises and model builders to maintain their respective keys within their own trust domains through Bring Your Own Key Management System (KMS) integrations so each party controls and enforces policy on its own assets. This protects an enterprise’s own fine-tuned weights, which are fast becoming critical IP, as much as a model builder’s base weights. Connected or Air-Gapped Deployment: Supports connected or fully air-gapped environments with DataEnclave deployments using attestation services built on the open CNCF Trustee stack, or in partnership with Fortanix via its Confidential AI infrastructure for fully sovereign AI. Governed and Auditable by Design: Records attestation events, key releases and enclave lifecycle actions in a tamper-proof, queryable audit trail in the VAST DataBase, providing visibility into what ran, where and under what verified policy without exposing protected data or weights. Secure Agent Sandboxes: The same DataEngine secure runtime provides isolated execution environments for AI agents through VAST AgentEngine, enforcing policy over the data, systems and tools agents can access and the actions they can take. Unlike people, agents are not accountable for their actions, so they need identity, a contained runtime and observability into when, how and why they each took action, plus auditability if something went wrong. Combining hardware isolation "Model weights are fast becoming the most valuable intellectual property in the world. Base weights define the value of frontier models, while fine-tuned weights will increasingly represent the proprietary intelligence of AI-driven enterprises," said Jeff Denworth, Co-Founder at VAST Data. "As the stakes get higher, so does the need to secure enterprise data so customers can apply the most intelligent AI models against it. Today, VAST Data - in partnership with NVIDIA - is moving the industry forward with a comprehensive approach to verifying previously untrusted computing environments and unlocking the ability to run any model against any data, anywhere." VAST DataEnclave extends confidential execution across the infrastructure where AI models and sensitive data are processed, combining hardware isolation and verifiable attestation to protect both while they are in use. Customer-controlled environments “Enterprise data is essential to accurate, usable AI – and keeping business data confidential is critical to protecting IP in the age of agents. VAST Data’s integration of NVIDIA Confidential Computing delivers protection for both enterprises and model builders, providing security, identity, permissions, governance and compliance as a foundation of the agent architecture.” – Justin Boitano, Vice President of Enterprise AI at NVIDIA. VAST is bringing together model builders, AI clouds, AI security, and infrastructure providers around a shared architecture for customer-controlled environments. Customers can now execute on their AI strategies without running into boundaries that previously cut off access to cloud-hosted models. At the same time, they can bring these newly available models within their own carefully constructed boundaries to manage cost, security, and other operational concerns. Confidential compute strategy “Nscale and VAST have worked together for years to build AI environments where enterprises and governments retain control of their data. DataEnclave takes that a step further, combining Nscale’s sovereign AI cloud infrastructure with VAST’s attestation-based architecture so frontier models and sensitive data can come together securely in a verified environment. That opens up workloads that were previously out of reach and expands what sovereign AI infrastructure can deliver.” – Tom Burke, Chief Revenue Officer, Nscale. "Customers around the world have unique regulatory and sovereignty requirements, and they are asking for AI that is encrypted end-to-end—not just at rest but in motion and during inference. Cohere has prioritised confidential compute for some time, and by working with VAST we can now bring that same level of security and governance to any data center, wherever customers choose to deploy. Together we are building a confidential compute strategy that gives customers more choice and more control, so they can run our models and the agents they build on North where their data already lives: their infrastructure, their jurisdiction, their rules." – Frank O'Dowd, Chief Revenue and Commercial Officer at Cohere. Most sensitive data “CrowdStrike SafeMind models are trained on the world’s largest pureplay cyber dataset, and that intelligence relies on the trust built around it. Defenders in regulated industries want to put these models to work against their most sensitive data, inside their own boundaries, while maintaining control of their data and protecting the models themselves. VAST’s attestation-based approach brings model weights and enterprise data together in a verified environment while keeping both protected and under their respective owners’ control. That’s what it takes to put frontier security models to work where the stakes are highest.” – Dr. Bartley Richardson, Chief AI and Autonomous Systems Officer, CrowdStrike. “The most sensitive data in the world sits in tables. Confidential computing turns trust from a promise into a proof, and that's the difference between AI that regulated industries can pilot and AI they can actually put into production. The guarantee comes from the architecture, not our word. Fundamental's Large Tabular Model, NEXUS, can run directly inside a bank's or health system's own infrastructure, in a sealed execution environment where our model and their records are exposed to neither party, working in environments we could never have reached otherwise." – Jeremy Fraenkel, CEO of Fundamental. "Video is where an enormous amount of institutional knowledge lives, and it is also the data that is hardest to move. Archives, sensor feeds and full-motion video sit in environments that are disconnected by design. TwelveLabs built our video intelligence models Marengo and Pegasus to run wherever that video already is, with no degradation in capability. With VAST DataEnclave, we can bring video intelligence into the most restricted environments while keeping our models protected and our customers' footage entirely under their control." – Jae Lee, CEO & Co-founder, TwelveLabs. Accelerated computing systems VAST DataEnclave also enables AI cloud providers worldwide to deliver attested, sovereign environments where model builders, enterprises, and governments retain control over their IP and data within their own jurisdictions. Because isolation is enforced in hardware, sovereign and regional AI clouds can establish verifiable trust without dedicating entire machines to a single tenant, and can offer the newest accelerated computing systems, such as NVIDIA Vera Rubin infrastructure, from facilities operated within national borders. This new VAST AI OS capability also provides OEMs the opportunity to deliver integrated confidential AI infrastructure that brings together trusted execution, accelerated computing and VAST-powered data infrastructure. VAST DataEnclave is being previewed today and will ship in Q1 2027 through VAST Data and participating OEM partners, including Cisco and Supermicro.

VAST Data, the AI Operating System company, announced the availability of VAST Foundation Stacks, a new open source library that augments and extends NVIDIA AI Blueprints into production-ready pipeline implementations that run natively on the VAST AI Operating System. NVIDIA AI Blueprints NVIDIA AI Blueprints provide developers with a foundational starting point for building advanced AI applications and intelligent agents, leveraging NVIDIA AI Enterprise software to rapidly prototype, customize, and deploy domain-specific AI workflows with minimal integration effort. VAST Foundation stacks extend these blueprints into production-ready templates, enabling organizations to deploy and operate NVIDIA-powered pipelines natively on the VAST AI Operating System. Developers can now focus on the business logic that connects AI to their environment instead of assembling the underlying infrastructure and platform layers required to support it, enabling teams to deliver AI applications faster. Enterprises are racing to operationalise proven AI patterns, but many reference examples still require extensive integration before they can run securely and reliably in production. Teams often must stitch together fragmented infrastructure, orchestration layers and data services to make these AI applications production-ready. VAST AI operating system VAST Foundation stacks address this challenge by extending NVIDIA AI Blueprints into repeatable, enterprise-ready implementations designed to run natively on the VAST AI operating system. By unifying data access, database services, compute orchestration, eventing, and pipeline execution in a single environment, VAST enables organizations to deploy scalable AI pipelines without building complex infrastructure from scratch.  These Foundation stacks can be seamlessly and repeatedly deployed anywhere the VAST AI OS runs, including in the cloud as well as on-premises via VAST's newly announced CNode-X platforms, as part of the NVIDIA AI Data Platform reference design.  Foundations stacks The first Foundation Stacks are based on NVIDIA AI Blueprints for Video Search and Summarisation (VSS) and NVIDIA AI-Q: The VSS-based VAST Foundation stack enables organizations to ingest massive volumes of live or archived video and extract insights through semantic indexing, summarisation, and interactive Q&A, powered by the high-performance data and pipeline services of the VAST AI Operating System.  The AI-Q based VAST Foundation stack provides a foundation for building custom AI researchers that can operate across private, enterprise data sources, synthesising hours of research in minutes while leveraging the VAST AI OS for persistent and secure context, scalable reasoning pipelines, and trusted agent execution. Expert insights “NVIDIA AI Blueprints have given the market an important starting point for building next-generation applications, but enterprises still need a production-ready way to deploy and operate those capabilities at scale,” said John Mao, Vice President, Global Technology Alliances at VAST Data. “With VAST Foundation Stacks, VAST is taking the architectural patterns behind leading NVIDIA Blueprints and giving customers a faster path from experimentation to production for scalable AI pipelines, video intelligence, and agentic AI systems.” “As enterprises transition to production AI at scale, preparing enterprise data for AI has become one of the biggest challenges,” said Adel El Hallak, Vice President, Product at NVIDIA. “Turning data into AI-ready pipelines needs to be done continuously and requires full-stack acceleration across compute, networking and software. By extending NVIDIA AI Blueprints with the VAST AI Operating System, customers can prepare and serve data so intelligent agents are always working off the most recent and accurate information.” Releasing stacks In addition to the available VSS and AI-Q implementations, VAST plans to release additional Foundation stacks in the coming months, including industry-focused examples.  VAST Foundation stacks will be available through a public GitHub repository, with interactive demos and planned sandbox environments for customers and partners.