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For 20 years, the resilience question Veeam answered was simple: Can I recover? In the agentic AI era, the question now is whether you can tell precisely what was affected, who or what touched it, and how to get back to a trusted state. Agents run for hours or days at a time, with the autonomy to reason through logical steps and make decisions without human oversight. They’re also adept at finding novel paths to their goals — paths that technically stay inside the boundaries of their harness but land somewhere their human operators never intended. An AI agent can follow every rule you wrote and still end up overwriting the data your business runs on.

That’s why security and trust at all levels has become the gating factor for large-scale agent AI adoption across the enterprise, and why security and resilience can’t be an afterthought in any rollout. Veeam helps enterprises protect and recover the data that lies behind AI agents.

Recently, NVIDIA introduced the NVIDIA Open Agent Safety Platform, an open software platform and reference system design that’s built to govern AI agents from testing through deployment. Veeam welcomes this open approach to agent safety, and Veeam is part of both the Open Agent Safety Platform launch ecosystem and the Open Secure AI Alliance, the Linux Foundation-governed coalition of more than 120 organizations. This is a real step forward for AI agent safety, and it shows why ecosystems matter: No single control point can make agentic AI safe on its own.

What is the NVIDIA Open Agent Safety Platform?

The NVIDIA Open Agent Safety Platform puts governance and enforcement outside the agent, where the agent can’t reason around it. It pairs a secure runtime, NVIDIA OpenShell, with an out-of-band hardware watchdog, NVIDIA Sentry.

• NVIDIA OpenShell is an open-source secure runtime that sandboxes each agent. Operators define which files, networks, tools, processes, and credentials an agent can use, and OpenShell enforces those limits as the agent works.
• NVIDIA Sentry is an out-of-band watchdog that runs on NVIDIA BlueField-4 DPUs and is built on NVIDIA DOCA. It monitors agent behavior from separate hardware and, according to NVIDIA, can quarantine an agent that leaves its boundary in milliseconds.

How Does Resilience Strengthen Agent Boundaries?

Boundaries and recovery are two halves of the same control. Boundaries shrink the blast radius; recovery closes what’s left. Agent “drift” — when long-running agents stray from their intended task — can’t be trained away without giving up capability, which is why enforcement at the agent needs a trusted data layer behind it. An agent can’t be expected to fully govern itself; policies can be too broad, and credentials can be compromised. An agent can act entirely within its permissions and still overwrite, delete, or corrupt the data your business depends on. When that happens, the question changes from “What did the agent do?” to “How fast can we get back to a known-good state?”

Most organizations can’t answer that question yet. Veeam’s Data Trust & Resilience Report 2026 shows that only 40% of leaders said they’re very confident they could isolate and precisely undo an AI mistake on enterprise data.

How Big is the AI Trust Gap?

Organizations are moving toward agentic AI faster than their trust foundation can keep pace. Veeam’s Data Trust & Resilience Report 2026, a survey of 600 senior leaders, found:

• 88% of organizations are already using or piloting AI agents, yet only 7% qualify as AI-ready.
• Only 28% are confident they could detect AI agents operating outside approved parameters.
• Over 82% of enterprises have unknown AI agents operating in their IT environments today.
• Just 40% are very confident they could isolate and precisely reverse an agentic AI failure.
• 95% of organizations say data challenges have already slowed their AI progress.

That’s the gap Veeam is built to close. Boundaries let us govern agents and protect the systems they interact with, but they have to be paired with comprehensive visibility into what agents actually do to data — and the resilience to reverse those changes when they’re wrong.

What Does Trusted Data Mean for Agentic AI?

Trusted data is data you can see, govern, protect, and recover. Veeam’s research defines four specific conditions: Clear visibility into where data lives and how it flows, enforced controls rather than policy alone, recovery that’s tested and validated, and executive alignment on ownership and accountability. Each condition is necessary; no one condition is enough by itself.

NVIDIA’s platform is a strong answer to enforced controls around the agent. The rest of the list lives at the data layer. NVIDIA’s own design principles point the same way: Labs, enterprises, and hardware providers each own their own layer, the way they do in the cloud today. The data layer belongs in that stack too since, in an agentic era, security starts with data.

Where Does Veeam Fit in the Agentic AI Stack?

Veeam, the Data and AI Trust Company, strengthens the trusted data layer that lies underneath the agents, models, applications, and pipelines that power enterprise AI. By bringing data security posture management (DSPM) together with data protection and recovery, Veeam helps enterprises protect data across its entire lifecycle and accelerate safe enterprise AI. Granular visibility and control is what moves an organization from policy as guidance to proof of operational controls. That means visibility into the data agents touch, governance over how it’s accessed, and clean, verified recovery with precision when something goes wrong.

• Know what agents can reach. Visibility across cloud, SaaS, and on-premises environments show where sensitive data lives, how it moves, and which identities and AI systems can touch it.
• Reduce risk before agents act. DSPM classifies sensitive information, maps access permissions, detects overexposed or misconfigured data, and surfaces toxic combinations of risk before they become AI-scale problems.
• Govern access with context. Consistent labels, policies, and least-privilege controls keep agents working only with data that’s approved, compliant, and appropriate for the task.
• Shrink the attack surface. Removing redundant, obsolete, and trivial data limits exposure, lowers operational noise, and leaves AI systems with a cleaner foundation to work from.
• Recover with precision. By connecting data, identity, AI activity, and protection context, Veeam helps teams restore only the affected data to a verified known-good state, without reintroducing the problem.

This matters most in AI factory environments, where data pipelines, models, applications, infrastructure, and autonomous agents run as one connected system. Trust in that environment is an operating model built on visibility, governance, enforced access, tested recovery, and executive accountability.

Veeam brings data backup, data security, and data recovery together across on-premises and cloud environments, which gives organizations visibility into and control over the security, governance, and resilience of their data and AI environments. Together, these solutions can support data resilience in AI factory environments by protecting the data and workloads that fuel AI, governing access, and supporting trusted operations across hybrid and cloud environments.

Put simply, NVIDIA helps control what an agent can do. Veeam helps make sure the data behind that agent can be seen, governed, trusted, and recovered. Together, boundaries and trusted data make enterprise AI safer to scale.

What Should Leaders Do Now?

Start with four moves. The first is for the executive table, and the other three are for the teams who will run the agents.

1. Name one owner for agentic AI risk. Today, accountability is fragmented. Veeam’s research found detection confidence is 24% higher when CISOs are responsible for AI agent risk.
2. Inventory your agents and what they can reach. Know every agent, model, and identity in your environment, including which data each one can touch. Start with the agents that have write or delete access.
3. Set recovery objectives for AI, then test them. Treat pipelines, vector stores, and the data that agents touch as production workloads with clear recovery targets. Run one drill: Simulate an agent overwriting a dataset, and time how long it takes to reach a known-good state.
4. Benchmark your AI readiness. Find out where you stand today and what it takes to close the gap.

Agentic AI should only scale as fast as enterprises can trust it. Boundaries and recovery together make that possible.

Take the Data & AI Trust Maturity Model assessment to see how ready your organization is for agentic AI.

FAQs

Learn More

• NVIDIA press release: NVIDIA Launches Open Agent Safety Platform to Secure Agents From Testing to Deployment
• NVIDIA Technical Blog: A Reference for Continuous In-Silicon Agent Monitoring
• Veeam Joins the Open Secure AI Alliance
• Data Trust and Resilience Report 2026

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