Mastercard executive flags data-in-use protection gap for AI agents on Kubernetes

Krishna Mayuram, senior principal engineer at Mastercard, told theCUBE Research that cloud engineering teams at financial companies face a security problem encryption alone cannot solve. This issue arises as AI agents move into production on Kubernetes, where container orchestration has become the default foundation for AI workloads.Krishna Mayuram identifies the data-in-use protection gap for Kubernetes-based AI agents as the critical security challenge for financial cloud engineering teams.

The facts

SiliconANGLE reported that the data these agents use is now the main target.reported Mayuram explained that payment networks feel this pressure more than most because every transaction carries customer data and regulatory exposure. Protecting data while it is being processed, not just while stored or moving, has become central to running agents at scale.

The facts

Red Hat has also brought confidential containers to OpenShift on bare metal. Mayuram noted that agents act like microservices and require the same elastic infrastructure capabilities, including horizontal and vertical scaling.

What to make of it

Mayuram described confidential computing as launching applications in highly secure, virtualised environments to protect data in use. He asked what happens to data in use after organisations have protected data at rest and in motion. Scale increases risk, especially for a company operating under many countries’ laws.

In practice

Cloud engineering teams at financial companies must address security problems that encryption alone cannot solve. Access to this protection involves using highly secure, virtualised environments or confidential containers. Availability depends on the underlying platform’s ability to provide these secure environments.