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Helping enterprises secure, govern, and recover AI systems with confidence as agentic workloads move into production.

Veeam is proud to join the Open Secure AI Alliance alongside NVIDIA and other industry leaders to advance secure, open innovation in agentic Al. As the Data and Al Trust company, Veeam is helping organizations confidently protect, recover, and govern the data to accelerate safe AI at scale. As enterprises adopt agentic AI, the control point has moved from the perimeter to the data itself. AI must be grounded in data that is secure, governed, and protected in order to drive trust and successful adoption. AI driven data leaks, breaches, compliance events or data loss from rogue agents can derail enterprise AI initiatives. Veeam’s Data & AI Trust Gap research underscores the urgency. While 88% of organizations are already using or piloting AI agents, only 7% qualify as truly AI-ready. Just 28% are confident they can detect AI systems operating across the organization, and 95% say data challenges have already slowed AI progress.

That is why the Alliance matters: trusted AI adoption requires an ecosystem working together. Veeam offers a single platform that unifies Data and AI Trust with security and resilience.

Why Data, AI Security, and Resilience must be unified

AI Systems have evolved and so have vulnerabilities and data risks. Data is both the key target for attackers and a critical attack vector that can be exploited. To drive secure AI adoption across the enterprise, organizations need visibility into data risks, proactive data controls to prevent incidents and the ability to respond quickly and precisely if data is compromised. The realities of attacks in the AI era highlight the need to bring security and resilience together to protect data across the entire lifecycle.

AI security incidents are operational risks that require a complete approach in order to see, prevent and respond to incidents. OWASP’s GenAI Exploit Round-up tracks AI incidents, from agent data leaks and supply-chain breaches to prompt-injection-driven exfiltration. A variety of mechanisms are used to execute these exploits. But what these exploits all have in common is they impact enterprise data. Data and AI systems must be protected from the ground up- Data must first be well understood and tightly controlled in order for AI guardrails to be effective. Security teams must know how data is being used and where the risks are, so they know what they’re backing up. Data resilience must be integrated into the solution to rollback to known good states rapidly and with precision.

Veeam’s Data Trust and Resilience Report 2026 found that 90% of security leaders are confident they can recover quickly, but only 69% say recovery objectives align with business continuity goals.

The security incident at Hugging Face, exposes the recovery gap. The experts could reconstruct what happened, but the harder question remained: how do you return the environment to a known-good state? A post-hoc analysis of logs revealed exactly how an attack unfolded- data was poisoned by malicious actors. But, without data resilience, how could an organization restore data to a known good state before the injection? Defenders need more than visibility into a compromise; they need a reliable way to contain the blast radius and return data to a trusted state without disrupting the business.

What Veeam brings to the Open Secure AI Alliance

Veeam brings a critical foundation for secure AI: trusted, resilient data. Following the Securiti AI acquisition, Veeam now unifies data resilience, DSPM, AI governance, and AI security through the Veeam DataAI Command Platform.

The goal is simple: help organizations know their data, secure it, govern it, protect it, and recover it so they can adopt AI with greater confidence. Recent innovation in Veeam Data Platform v13.1, including more than 70 new features and enhancements, further advances this strategy by helping organizations recover clean and fast from cyber disruption.

The Veeam DataAI Command Platform Delivers:

• Continuous discovery and inventory of data and AI assets — agents, models, data systems, and human and machine identities — across clouds, SaaS, and on-prem, so visibility keeps pace with a sprawling estate instead of trailing it.
• Highly accurate, context-aware classification of sensitive and regulated data, across structured and unstructured systems — replacing weak regex-based tagging with labels precise enough for copilots and agents to use data safely.
• Contextual understanding of data, AI, Identities and Access relationships — which human and machine identities can access sensitive data, and how permissions and configurations shape data and AI security posture — surfacing toxic combinations of risk that single-point tools miss.
• Enterprise AI data risk intelligence and remediation — policy-based approach to continuously monitor, report and remediate AI data risks. Security can enforce robust, consistent controls across data and AI systems: contextual classification and labeling, least-privilege data access governance, ROT data minimization, data sanitization, and automated compliance testing and policy enforcement. AI initiatives launch on data verified safe and compliant, without re-architecting security for every project.
• Precision recovery when AI systems are compromised — Veeam connects data, identity, AI activity, and protection context so teams restore only affected data to a verified known-good state, without reintroducing the exposure that caused the incident.

Resilience for the open-source infrastructure powering the AI ecosystem for enterprises

Veeam’s protection of opensource software and infrastructure starts below the model layer, where AI ecosystems depend on Kubernetes, storage and data resilience they can trust. This is the layer Veeam has protected and recovered for years.

Through Veeam Kasten, Veeam created and donated Kanister, the CNCF project for application-level Kubernetes data protection. As AI factories run stateful workloads, its blueprints now cover vector databases like Qdrant, PGVector and Weaviate — the data stores behind RAG and agentic AI — so those workloads are recoverable by design, alongside the traditional databases Kanister has long protected.

Veeam also maintains Kubestr for Kubernetes storage benchmarking and uses Kopia as a secure, encrypted backup engine to harden the storage layer AI factories depend on.

For enterprises, this matters because AI systems and factories can only scale when the open-source infrastructure beneath them is resilient, recoverable and trusted. By building protection into Kubernetes, storage and AI data services early, organizations can accelerate adoption of AI while reducing operational risk and avoiding costly rebuilds after disruption.

Strategic impact: trusted AI requires recoverable data

Veeam’s participation in the Open Secure AI Alliance is to help make recoverability a core requirement for enterprise AI security. Open models and open evaluation tools can improve visibility and control, but successful AI adoption by enterprises depends on the ability to govern data, contain risk and restore compromised AI workloads, pipelines, and infrastructure to a verified known-good state. For enterprises scaling agentic AI, the outcome is clear: faster recovery, stronger governance, and greater confidence that AI innovation can move into production without increasing business risk. That is where Veeam helps the Alliance’s mission from detection to operational resilience.

Ready to build AI you can trust? Learn how Veeam helps organizations close the Data and AI Trust Gap, benchmark AI readiness and build the resilient foundation needed to secure, govern and recover AI at scale with Veeam’s Data & AI Trust Maturity Model.

The post Veeam Joins the Open Secure AI Alliance to Drive Agentic AI with a foundation of Trusted Data appeared first on Veeam Software Official Blog.

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