The missing layer in enterprise AI: Why data trust is becoming the new priority

Enterprise AI has moved from boardroom discussions to active deployment. Organisations across industries are investing in AI adoption, exploring new use cases and building strategies around automation and intelligence. Yet despite the momentum, many initiatives continue to struggle.

According to Sandeep Bhambure, MD & VP, India and SAARC, Veeam Software, the biggest challenge is not necessarily the AI model itself. The real issue is whether organisations can trust the data feeding those systems.

As enterprises accelerate AI adoption, the conversation is expanding beyond infrastructure and models to include data resilience, AI governance, AI security and cyber resilience. At the centre of that discussion is what Bhambure describes as a missing but critical layer: data AI trust.

Data AI trust is emerging as the foundation of enterprise AI

The evolution of enterprise AI is creating new expectations around how organisations manage, protect and govern their information assets.

During VeeamON, the company announced its Data AI Command Platform, a solution designed to combine data resilience capabilities with AI security and governance functions.

Bhambure believes that while organisations have focused heavily on infrastructure, compute resources, large language models and AI agents, they have overlooked a fundamental requirement.

"What we believe was missing in this whole AI stack was data and AI trust," he said.

According to Bhambure, the absence of trusted data is one of the biggest reasons many AI projects fail to deliver expected outcomes.

"The fact is, today 90% of the projects are failing because the data that is feeding these AI projects cannot be trusted."

The platform is designed to provide organisations with security, compliance, governance, privacy and resilience capabilities from a single environment. For enterprises seeking to scale AI adoption, trusted data is increasingly becoming the foundation upon which successful AI initiatives are built.

Why AI governance and DPDP compliance now depend on trusted data

As AI adoption accelerates, AI governance and DPDP compliance are becoming closely linked.

Bhambure argues that organisations often view compliance through a narrow lens, focusing primarily on consent management. However, he believes compliance requirements extend much further and require comprehensive visibility into enterprise data.

"What is important is to have a clean line of data, clean and trustworthy data. That is the foundation, whether you want to drive your AI projects or comply with DPDP requirements."

He explained that organisations need visibility across every element of their data estate, whether information resides in Cloud environments, SaaS applications, on-premises infrastructure or backup systems.

Data visibility and contextual intelligence are becoming critical for AI governance

Visibility alone is no longer sufficient. Organisations must also understand the context surrounding their data.

According to Bhambure, enterprises need to know what type of data they possess, who can access it, which systems interact with it and increasingly, which AI agents are authorised to use it.

The growing influence of AI is also changing the threat landscape.

"Four out of five attacks today happen from AI agents."

As a result, data visibility, observability, classification and contextual intelligence are becoming essential components of modern AI governance and compliance strategies.

Data resilience and AI security challenges organisations cannot ignore

While AI creates new opportunities, it also introduces new vulnerabilities.

Bhambure noted that a significant percentage of organisations have experienced cyberattacks over the past year. At the same time, AI workloads are introducing additional security considerations around access management, privilege control and data protection.

The ability to maintain data resilience remains one of the most important safeguards against emerging threats.

He emphasised the importance of maintaining immutable copies of critical information and adopting proven cyber resilience practices to protect enterprise environments.

Why over-privileged AI agents create new AI security risks

One of the most pressing concerns involves access permissions.

"97% of the agents are over-privileged in terms of their access."

According to Bhambure, organisations need greater visibility into how permissions are assigned and managed across both human users and AI agents.

Without proper controls, excessive privileges can expose sensitive information and increase organisational risk.

The ability to identify and eliminate potentially dangerous combinations of permissions is becoming increasingly important as enterprises expand AI deployments.

How data recoverability strengthens cyber resilience

Data recoverability remains a critical element of cyber resilience.

Bhambure warned that many organisations inadvertently reintroduce compromised data into production environments during recovery efforts following ransomware incidents.

"More than 50% of customers, while recovering from ransomware attacks, tend to re-infect the production data."

To reduce that risk, organisations need the ability to identify clean copies of critical information and validate recoveries before restoring systems to production.

Bhambure highlighted the importance of using isolated clean-room environments to verify that recovered data is free from malware before reintroducing it into operational environments.

Building a resilient AI ecosystem through partnerships and skills development

Creating a resilient AI ecosystem requires more than technology.

Bhambure discussed the importance of collaboration between industry stakeholders, technology providers and institutions to strengthen cyber resilience and improve AI readiness.

One of the initiatives announced by the company involves a partnership with DSCI aimed at strengthening cyber resilience capabilities across the industry.

According to Bhambure, awareness, education and capability building are essential as organisations prepare for increasingly sophisticated AI-driven threats.

Preparing organisations for the agentic AI era

The emergence of agentic AI is creating demand for new skills and expertise.

Bhambure highlighted programmes designed to support workforce development and improve industry preparedness for future AI and cyber resilience requirements.

These initiatives focus on expanding talent pipelines, supporting educational institutions and helping organisations build capabilities required for the next phase of AI transformation.

For Bhambure, preparing for the agentic AI era is not simply about adopting new technologies. It is about ensuring organisations possess the skills, governance frameworks and resilience capabilities needed to use those technologies responsibly.

Why data AI trust could accelerate AI adoption at scale

As organisations continue investing in enterprise AI, the focus is shifting from experimentation to accountability.

Business leaders increasingly need assurance that AI systems are secure, compliant and resilient. Questions around data poisoning, governance failures and hallucinations are becoming board-level concerns.

According to Bhambure, resilience is becoming the safety net that enables organisations to move faster with AI adoption.

"The resilience aspect of AI is the single most important thing which is required for unleashing the power of AI."

He compared AI resilience to safety mechanisms in a vehicle.

"It's like you have the seat belt on and all the safety measures. Only then do you feel that you can accelerate on a highway. If those guardrails are not there, you're not going to."

The message is clear. Organisations may invest heavily in infrastructure, models and applications, but without trusted data and strong resilience capabilities, achieving AI at scale becomes significantly more difficult.

Conclusion: Data AI trust may become the defining factor for enterprise AI success

The next phase of enterprise AI will not be determined solely by model performance or computing power.

According to Bhambure, success will increasingly depend on whether organisations can establish trust in the data that powers their AI systems.

As AI governance, AI security, DPDP compliance and cyber resilience become interconnected priorities, data AI trust is emerging as the missing layer capable of supporting long-term AI adoption.

Summing up the company's vision, Bhambure said, "Veeam is introducing the Data AI trust layer, which is important for unleashing the power of AI."

For enterprises looking to accelerate AI adoption with confidence, trusted data may prove to be the most valuable asset of all.

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