AI workloads do not need a new network category. They need the same fundamentals, at higher stakes.
In this video, Graphiant CSO Arsalan Khan walks through three security-led use cases built for AI-era data movement.
- One fabric for every endpoint. Khan treats public clouds, data centers, edges, and AI neoclouds as the same problem: any-to-any connectivity. The payoff is less backhaul and cleaner paths for high-bandwidth, low-latency traffic. You will also see how Graphiant approaches private cloud on-ramps through options like AWS Direct Connect and Azure ExpressRoute, with visibility through the portal plus APIs for your tooling.
- Partner exchange without the setup tax. Most partner links fail on time, expertise, and messy realities like overlapping IP space. The walkthrough covers how Graphiant handles NAT and onboarding for partners on the fabric and off the fabric, plus the operational control to revoke and restore access fast when a partner incident hits.
- Governance and threat context, built into the network. Data Assurance moves policy from slide decks into enforcement. Khan explains geo-boundary rules, app-level classification, and role-based access so governance teams audit while engineers build. For threat signals, the model leans on public intel plus behavior, without decrypting payloads.
Breach costs keep rising, with IBM reporting a $4.88M global average in 2024. If your AI program moves sensitive data across clouds and partners, this video shows how to keep speed without losing control.
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