Veeam Software, the global pioneer in Data and AI Trust, has introduced its Data and AI Trust Maturity Model. Announced at VeeamON 2026, this data-driven model offers a complete guide on how companies can measure and secure their AI governance practices amid the change from co-pilot AI to machine-speed AI autonomy.
Bridging the AI Implementation Gap
While AI adoption has reached a saturation point across most industries, a significant disparity exists between initial deployment and effective governance. As AI agents move toward making autonomous decisions on enterprise data, the lack of robust identity frameworks and data foundations has become a material risk.
Recent research conducted by Emerald Research Group on behalf of Veeam highlights that organizational adoption has far outpaced the implementation of the controls necessary to justify AI decisions to regulators, auditors, or boards of directors. The focus is shifting from simple usage to the urgent need for transparency, control, and validation of AI actions.
A Prescriptive Path to Production-Ready AI
The Data and AI Trust Maturity Model offers leaders an independent, objective lens to evaluate their current standing. By moving beyond experimentation, the model helps organizations build the accountability required for “agentic” AI environments.
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Model Highlights:
12 Critical Dimensions: Evaluates maturity across a broad spectrum of data, identity, and security pillars.
Five Evolutionary Stages: Maps organizational progress from initial “ad hoc” efforts to “leading” industry standards.
Prescriptive Benchmarking: Identifies precisely where controls exist and where they are likely to fail under real-world pressure.
“AI confidence is high, but confidence alone does not scale,” said Anand Eswaran, CEO at Veeam. “Our research shows that while most organizations believe they are ready to scale AI safely and responsibly, many struggle to demonstrate that readiness in a board, audit, or regulatory context. The Data and AI Trust Maturity Model provides leaders with a clear, objective way to understand where they truly stand, identify execution gaps, and prioritize the capabilities required to operationalize AI trust, not just aspire to it. This is critical in an agentic world.”
The Role of the Data Foundation
Industry experts emphasize that AI reliability is inextricably linked to the resilience of the underlying data layer. With attackers increasingly targeting data through inference and corruption, technical controls must be directly tied to business outcomes.
“AI success hinges on the strength of the data foundation, but that’s exactly where organizations are exposed,” said Krista Case, Principal Analyst at theCUBE Research. “While three-quarters of organizations are already running maturing or operational AI deployments, fewer than a third are backing up even half of their AI-generated data, according to our research. And that’s translating directly into real risk. Attackers are going straight after the data layer through inference, corruption, poisoning, and exfiltration. Practitioners need structured, benchmarked insight that ties technical controls to real business and regulatory outcomes. Veeam’s Data and AI Trust Maturity Model bridges this gap.”
The Data and AI Trust Maturity Model is available immediately to help organizations transform AI from a perceived asset into a measurable, governed capability.