Beyond the hype: Accelerating Enterprise AI with Secure Data

Every AI initiative relies on a foundation of secure data. Copilots and agents don’t check whether a file was over-shared, over-retained, or never classified — they reach whatever their permissions allow and surface it at machine speed. Where data security controls are inconsistent, AI doesn’t just inherit the gaps — it amplifies them.

Analyst Paul Stringfellow has evaluated the technology of myriad data and AI security vendors to find out who delivers real readiness — and who is just checking the box. Hosted by Nikhil Girdhar, Senior Director of Product Marketing at Veeam, Paul unpacks his headline findings, why DSPM has become a boardroom priority, and what AI-ready data security actually requires: knowing your data, controlling who and what can reach it, and proving both, continuously.

Join this session to learn

• Why data security readiness is the first — and most often skipped — step of safe AI adoption
• How copilots and agents inherit and amplify gaps in data security controls
• What separates AI-ready data security posture from checkbox compliance
• The Radar’s headline trend and what it signals about where the market stands
• Your first move for making your data estate AI-ready before your next rollout or audit

AI is magnifying risks that were always hidden in your data — over-shared files, inherited permissions, unclassified sensitive content — and surfacing them at machine speed. These risks are invisible to tools built for an earlier era, because AI risk is highly contextual: it emerges where data sensitivity, identity, and AI use intersect. And the scale is compounding fast: machine identities already outnumber human identities 82 to 1.

Jennifer Glenn, Research Director at IDC, has spent years researching data security. Hosted by Mukul Hinge, Product Marketing Principal at Veeam, Jennifer shares what’s actually changing in data and AI security, why the market is consolidating around a few key drivers, and why deep contextual insight — which AI can reach what data, under whose identity, governed by which rules — is what lets you systematically identify risks before they become incidents and build an AI risk posture you can defend to the board.

Join this session to learn

• What is actually changing in data and AI security — and what’s noise
• Why AI adoption is outpacing security teams’ visibility, and how to close the gap
• Why AI risk is contextual: how risk scenarios emerge at the intersection of data, identity, and AI use
• Where the market is headed: consolidation around a few key drivers for AI security
• How to build confidence in — and defensibility of — your AI data-risk posture

Every agent, copilot, and model in your enterprise interacts with one shared resource: your data — the input, the output, and the thing at stake when something goes wrong. Securing it across its entire lifecycle, from discovery and classification through AI consumption to recovery, is the basis of Data & AI trust. That means data security and data resilience can no longer live as separate programs: integrated, they are what lets an organization trust its data — and its AI — at scale.

Hosted by Todd Vancil, VP Systems Engineering, and Chris Joynt, Director of Product Marketing for AI Security Solutions at Veeam, Todd Thiemann of Omdia Research presents new findings on where organizations struggle to bridge Data & AI trust gaps — and, drawing on real deployments, what an integrated approach looks like in practice: accelerating AI with data that is secure, compliant, and recoverable.

Join this session to learn

• Where real-world deployments struggle to bridge Data & AI trust gaps — and how leaders close them
• Why data is the one resource every agent shares — and why lifecycle security is the basis of Data & AI trust
• How integrating data security and data resilience turns two programs into one strategic capability
• What a unified, lifecycle approach looks like in practice, and how to build it

The data and AI security landscape is moving faster than any planning cycle. Agent capabilities that didn’t exist a year ago are now in production. Regulations are being drafted around technology still taking shape. The vendor market is consolidating quarter by quarter. For security leaders, the challenge isn’t reacting to each change — it’s seeing far enough ahead to plan for what’s coming.

Rehan Jalil, Veeam President of Products and Technology, closes the series with a grounded look at where data and AI security is headed in 2027: the shifts that will actually matter, the ones that won’t, and the practical moves forward-thinking leaders can make now to stay ahead of both. He’ll draw the threads of the series together — the data foundation, the agent-era risk model, the integration of security and resilience — into a clear-eyed view of the year ahead.

Join this session to learn

• Where data and AI security is headed in 2027 — and which of today’s trends will matter most
• How agentic AI will reshape the security operating model, budgets, and the vendor landscape
• Where regulation and AI governance are going — and how to get ahead of both
• The practical moves to make now so your 2027 plan stays ahead of the landscape