AI can accelerate legacy system modernization with data flowing across aging infrastructure, hybrid environments, and newly cloud-native workloads. For many DAF organizations, the challenge is not simply migrating applications; it is reducing technical debt while preserving visibility, controlling cost, and avoiding lock-in to cloud-specific monitoring approaches that may change as mission, performance, or budget requirements evolve.

This session will examine how a virtual TAP-based visibility layer can support AI-driven modernization by providing consistent network-derived intelligence across legacy data centers, virtualized environments, and  cloud architectures. Rather than relying exclusively on native cloud telemetry that varies by provider, pricing model, and feature set, organizations can use a common visibility fabric to feed security, operations, and performance tools with the traffic and metadata they need often at a lower cost. That approach helps teams compare costs more effectively, reduce redundant tooling, improving tool performance, and maintaining flexibility when shifting workloads or changing analytics platforms.

Attendees will learn how this model can help with modernization of legacy DAF infrastructure while supporting cloud migration, AI-assisted operations, and long-term readiness. The discussion will focus on practical strategies to create a stable, tool-agnostic source of high-fidelity telemetry that can feed SIEM, NDR, performance monitoring, and analytics platforms without requiring a redesign each time the architecture changes. The result is a modernization approach in which visibility remains the constant, even as applications, clouds, and mission demands continue to evolve.