Conventional network segmentation and access control stay fully applicable to AI applications, and they extend to cover several AI-specific communication patterns that deserve their own explicit attention: connections to model provider endpoints, connections tool calls make to external services, and any browsing or web-fetching capability an agent might have.
Restrict outbound connections to known model provider endpoints
Rather than letting an AI application's backend have broad outbound network access, restrict its outbound connections to the specific, known model provider endpoints it actually needs, using the same principle of least privilege running throughout this content series applied at the network layer. This limits the potential impact if the application's own code is ever compromised, since a compromised process with restricted outbound access has fewer options for exfiltrating data or reaching an attacker-controlled destination.
Apply egress restrictions to tool-initiated network calls specifically
Any tool making its own outbound network calls, calling a third-party API, fetching a resource, deserves specific egress restriction limiting what destinations it can reach, following the network boundary discussion in earlier architecture guidance. This matters a lot for tools with any generality in what destination they call, since an insufficiently restricted tool can become an exfiltration path, covered in data exfiltration guidance, if a manipulated model steers it toward an attacker-controlled destination.
Restrict browsing and web-fetching tools to a defined allowlist where feasible
For agents with a browsing or web-fetching capability, restrict the domains or destinations it can reach to a defined allowlist relevant to the agent's actual task, where the use case allows this kind of restriction, following the browsing-specific risk discussion in earlier prompt injection guidance. Where the use case genuinely needs open web access, apply the strongest available content-level scrutiny to fetched content instead, since domain-level restriction isn't always feasible for a genuinely general-purpose browsing capability.
Segment AI infrastructure from broader internal networks appropriately
Apply conventional network segmentation to isolate AI application infrastructure, particularly components handling sensitive retrieval content or credentials, from broader internal networks, limiting lateral movement opportunity if any specific component gets compromised, following standard network security practice extended to cover these AI-specific components explicitly rather than assuming they inherit appropriate segmentation automatically.
Monitor network traffic patterns for AI-specific anomalies
Beyond conventional network intrusion detection, monitor for network traffic patterns specific to AI components: unexpected destinations from a tool that should only reach a narrow, known set of endpoints, or unusual volume patterns from a model provider connection that might point to the cost and resource anomalies covered in monitoring guidance elsewhere, showing up at the network layer.
Apply TLS and encryption consistently for AI-specific traffic
Make sure all traffic to model providers, vector databases, and any external AI-related service uses proper encryption in transit, the same standard already applied to any other sensitive service connection, since prompt content and retrieved data traveling to these destinations carries the same sensitivity covered throughout data protection guidance and deserves the same transport-level protection.
Review third-party AI vendor network access specifically
For any third-party AI vendor or tool integration needing network access into your own infrastructure, review that access with the same scrutiny you'd apply to any third-party network integration, scoping it as narrowly as the integration's actual function requires, connecting to the vendor assessment discussion in governance guidance elsewhere.
Test network controls directly, not only through configuration review
Periodically test that network restrictions actually hold as configured, attempting connections from a restricted component to a destination it shouldn't be able to reach, rather than leaning solely on configuration review to confirm the intended restrictions are actually in effect, following the direct verification discipline running throughout secure development guidance.
Where TELEON fits
TELEON's audit trail, applied through the gateway or supported middleware, can log tool call activity including destination information for tools that make outbound calls, supporting network anomaly monitoring alongside dedicated network-layer monitoring tools.
Configuring specific network segmentation, egress restrictions, and domain allowlists remains infrastructure engineering work that has to be implemented directly within each organization's own network architecture.
A short checklist
- Restrict outbound connections to known, specific model provider
endpoints.
- Apply egress restrictions to tool-initiated outbound network calls.
- Restrict browsing tools to a domain allowlist where the use case
allows it.
- Segment AI infrastructure appropriately from broader internal
networks.
- Monitor network traffic for AI-specific anomaly patterns.
- Apply consistent encryption in transit for all AI-related traffic.
- Review third-party AI vendor network access with the same scrutiny as
any integration.
- Test network restrictions directly rather than relying solely on
configuration review.
Network controls for AI applications extend conventional segmentation and egress restriction practice to cover the specific communication patterns AI components introduce, model provider connections, tool-initiated calls, browsing capability, each deserving explicit attention rather than an assumption that general network security already covers them adequately.