Private AI. Where does your data go?

Your AI runs in a private cloud. Where does your data go next?

In another post, I explored sharing private AI infrastructure while keeping clear boundaries between departments.

Now, let’s follow the data beyond those boundaries.

An AI application might retrieve internal documents, call an external model, and connect to a business system. Each connection needs its own permissions, controls, and visibility.

Vendors are addressing different parts of this challenge:

• Fortinet FortiAIGate, available through a validated architecture with Equinix’s Distributed AI Hub, inspects AI interactions and applies security policies across distributed environments.
• Cisco AI Defense provides runtime protection for AI applications and agents across cloud and on-premises deployments, including controls against prompt injection and sensitive data leakage.
• Palo Alto Networks Prisma AIRS AI Runtime Security inspects live AI interactions to detect threats and protect applications, models, and data.

These are examples from a wider range of available solutions. Their capabilities and deployment requirements differ.

My takeawayDesign security around the complete AI workflow, from the first request to the final action.

Map your connections. Define permitted data flows. Enforce access controls. Inspect sensitive interactions. Test performance with security enabled.

Select solutions against your architecture, data, and business risks.

Sharing resources safely and preserving protection across every connection require coordinated decisions.