A Blueprint for Securing Networks for AI : From Architecture to Operations#

Saju Salahudeen – NOKIA, Reda Laichi – NOKIA

• Why: The only talk focused specifically on securing AI backend fabrics, where RDMA bypasses the host stack — forcing enforcement down to the switch and DPU. Core to the PM's network-security + AI-fabric overlap.
• What: A Day 0/½ framework. Day 0: SZTP (RFC 8572, ownership vouchers, IDevID on TPM) over insecure ZTP, secure/measured boot chains. Day 1: TLS for syslog/gRPC/RADIUS/NTP, gRPC RPC restriction via gNSI Authz (allow gNMI Subscribe, deny gNMI Set), AAA hardening, per-tenant VRFs on switches plus per-flow DPU policy pushed per job, backend default-deny ACLs (RoCEv2 UDP 4791 within same job/tenant only), DDoS tiers (edge scrubbing/Flowspec/RTBH, API-key rate limits for inference, PFC watchdog and per-tenant QoS against pause storms, CoPP). Day 2: monitoring shifts from packet inspection to streaming telemetry (sFlow, INT) plus behavioral baselining, because DPI/host IDS don't scale to 400G/800G and RDMA traffic is invisible to host-based tools.
• How: Anchored in the OpenAI/Hugging Face incident (agents exploited a third-party artifactory via exposed HF credentials) and in a public multi-vendor reference lab (github.com/srlinuxamericas/n98-ai-security).
• Where applicable: Vendor session (Nokia principal engineers) with framework flavor, but the techniques are multi-vendor and the lab is public; applies to any shared GPU fabric where kernel-bypass transports defeat host-based controls.