As the enterprise world accelerates toward intelligent automation, Agentic AI is emerging as the next major inflection point. In this insightful interview, Ben Young, Field CTO – APJ at Veeam, explores how this paradigm is redefining digital resilience and operational agility across industries.
Q: How do you see the rise of Agentic AI, and what sets it apart from conventional AI in enterprise cases?
A: The key difference with Agentic AI is autonomy—it doesn't just analyze and recommend, it actually executes. And these agents can collaborate with each other to tackle complex, multi-step tasks. While traditional AI might suggest a backup restore point, Agentic AI triggers the restore, validates integrity, and orchestrates failovers during attacks.
What makes these agents truly powerful is their ability to take on specific personas and tasks, then collaborate with other agents until complex objectives are completed. Think of it as an intelligent workforce that never sleeps.
Q: Which major verticals are leading the adoption of Agentic AI and why?
A: Industries where downtime equals immediate risk and where masses of data need rapid processing are leading the charge—after all, AI's power relies heavily on performance at scale. Banking and financial services are using Agentic AI for fraud detection and compliance automation. Manufacturing is leveraging it for predictive maintenance and supply chain optimization.
India is particularly aggressive in this space, with BFSI and e-commerce driving massive investments. BCG projects India's AI market will surpass $17 billion by 2027—more than tripling current levels. Across every vertical, the foundation remains the same: data is the fuel that powers these agentic systems. But as AI becomes more autonomous, supply chain risks like data poisoning become critical concerns.
Q: What cybersecurity risks or governance concerns do you foresee with Agentic AI deployments?
A: Autonomy is both Agentic AI's greatest strength and its greatest vulnerability. A compromised agent could lock out legitimate users or delete critical datasets. We're already seeing sophisticated attacks targeting AI systems.
But here's the deeper challenge: AI workloads are pushing data outside traditional IT perimeters. Kubernetes deployments, vector databases, S3 buckets—often managed by application teams rather than infrastructure teams. This creates massive data exposure risks. Through Coveware by Veeam, who handle cyber extortion cases daily, we see that data exfiltration and theft are rising significantly alongside traditional ransomware attacks. These threat actors fully understand the value of data. While governance frameworks for autonomous agents are still evolving, one thing is clear: when AI agents make mistakes or entire AI systems go down, rapid recovery is everything.
Q: One piece of advice you’d offer to CIOs planning to integrate Agentic AI into their digital roadmap?
A: Start with your data foundation, not your use cases. Too many CIOs get excited about the possibilities—autonomous decision-making, intelligent workflows—without first asking: "Is my data ready for this level of autonomy?”
Agentic AI is only as reliable as the data it operates on. If your current data is fragmented, poorly governed, or inadequately protected, autonomous agents will amplify those problems at scale. Before you deploy a single AI agent, ensure your AI infrastructure can support both the performance demands and the resilience requirements of autonomous systems.
My practical advice? Audit your current data protection strategy against your AI ambitions. Can you recover quickly when an agent makes a mistake? Can you provide clean, historical data for training and context? Can you protect against the new attack vectors that come with autonomous systems? If you can't confidently answer yes to these questions, focus there first. The most sophisticated AI strategy in the world won't survive a single data integrity failure.
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