AI Networking Strategy: Data Exchange, Assurance, GINA

AI projects break when your data moves faster than your network governance.

In this session, Graphiant Chief Product Officer Vinay Prabhu lays out a two-part approach.

1. Build a network for AI.
2. Then use AI for the network.

Network for AI starts with a hard truth. AI behaves like a distributed publisher and subscriber system. Data originates in one place. Inference runs somewhere else, often outside your business boundary. Vinay shows how Graphiant targets the bottleneck most teams hit first. Partner data exchange.

You will see Graphiant position data exchange like a marketplace. You publish a service. Your partner subscribes. You bring the connection up fast, without weeks of back-and-forth on technical details. The point is simple. You move from bespoke, one-off partner builds to a repeatable workflow with governance built in.

Then Prabhu shifts to trust. He uses a ride-sharing analogy to make a clean point. You do not accept blind transport for high-value data. You want visibility into who connected, when, where traffic went, and whether the path stayed inside your rules. The video highlights observability, auditability, and centralized control, with policies programmed onto the global fabric, not stitched together across disconnected domains.

The second pillar, AI for the network, is GINA, the Graphiant Intelligent Network Assistant. Prabhu frames GINA as an ops teammate that turns routine work into fast answers. Monthly compliance reporting, threat-focused summaries, and infrastructure health, without pulling data across multiple dashboards and teams. Gina AI is built into the service for policy enforcement, observability, and compliance reporting.

If you care about AI readiness, this video focuses on outcomes. Faster partner exchange. Clear proof for compliance conversations. Less time spent assembling evidence. More time spent operating.

Watch the full talk to see how Graphiant can empowers your AI ambitions.