Fortinet's Secure AI Data Center: End-to-End Security for Models, Data, and Infrastructure
Fortinet has introduced the Secure AI Data Center solution, a full-stack security framework built to protect AI data centers-from network fabric to applications and large language models (LLMs). It delivers advanced threat protection with ultra-low latency and, according to Fortinet, reduces power consumption by an average of 69% versus traditional approaches.
At the core of the launch is the FortiGate 3800G, a next-generation data center firewall built for high-density GPU clusters. It uses Fortinet's NP7 and SP5 ASICs with 400 GbE connectivity to maintain throughput at scale while keeping energy costs in check.
Why this matters for product development leaders
- Shipping AI features safely requires throughput, predictability, and controls that don't slow teams down.
- Guardrails for LLM inputs/outputs help prevent prompt injection, data leakage, and misuse before they reach production.
- Integrated security and operations reduce context-switching across tools and speed up incident response.
- Lower energy per Gbps helps you scale GPU clusters without blowing up OpEx.
What's in the Secure AI Data Center framework
- ASIC-accelerated segmentation and performance: Hyperscale throughput with significantly lower energy per Gbps to support dense AI traffic patterns.
- Scalable AI infrastructure protection: Real-time defense for large GPU clusters with 400 GbE and ASIC-driven efficiency.
- LLM security and data protection: Manage model traffic and enforce strict input/output guardrails across on-prem, hybrid, and cloud.
- Operational efficiency via Fortinet Security Fabric: Unified firewalling, application security, and AI runtime protection under one platform to simplify audits and daily operations.
- Granular application and API security: Layered inspection and traffic sanitization to prevent code injection, tampering, and data theft.
- Quantum-safe security: Post-Quantum Cryptography (PQC) and Quantum Key Distribution (QKD) to help future-proof data confidentiality.
FortiGate 3800G at a glance
- NP7 and SP5 ASICs designed for data center-scale inspection and throughput.
- 400 GbE connectivity for east-west and north-south traffic in GPU-heavy environments.
- Real-time protection for high-density clusters with strong energy efficiency.
"AI data centers require unprecedented levels of performance without compromising security," said Nirav Shah, Senior Vice President of Products and Solutions at Fortinet. "Our Secure AI Data Center solution brings these two critical requirements together-combining ASIC-powered firewalls like the FortiGate 3800G with advanced protections for data, applications, and LLMs-so organizations can scale AI confidently, efficiently, and securely."
Performance snapshot (Security Compute Rating)
- Firewall throughput: 800 Gbps (competitors avg: 240 Gbps)
- IPsec VPN: 210 Gbps (competitors avg: 96.3 Gbps)
- Threat Protection: 200 Gbps (competitors avg: 90 Gbps)
- Concurrent sessions: 200M (competitors avg: 42.3M)
Threat Protection results were measured with Firewall, IPS, Application Control, Malware Protection, and Logging enabled. Competitive numbers are from publicly available sources; testing methods may vary.
Practical steps for your roadmap
- Map AI data flows: Identify where prompts, embeddings, model outputs, and sensitive datasets move-and who can access them.
- Segment GPU clusters: Use zero-trust segmentation at the rack/pod level and enforce strict east-west policies.
- Enforce LLM guardrails: Apply input/output validation, prompt filtering, and data loss controls for model endpoints.
- Set performance budgets: Benchmark end-to-end latency with full inspection enabled to avoid surprises in production.
- Instrument runtime security: Centralize telemetry for fast triage across firewalls, APIs, apps, and model gateways.
- Plan for PQC: Build a migration path to PQC algorithms and evaluate QKD for high-sensitivity data channels.
- Test continuously: Add prompt-injection and data exfiltration test cases to your CI, plus red-team scenarios for model misuse.
Compliance and long-term resilience
The framework supports security controls that help align with emerging AI oversight while keeping GPU utilization high. PQC and QKD integration add a path forward against future cryptographic risks. For background on PQC, see NIST's program overview at NIST.
If your team is building or securing AI products and needs to upskill on guardrails, model security, and AI operations, explore role-based learning paths at Complete AI Training.
Customer perspective
"AI is transforming every aspect of our operations, from product innovation to supply chain optimization, while introducing new security challenges," said Huy Ly, Head of Global IT Security at Monolithic Power Systems. "Fortinet's Secure AI Data Center solution gives us the visibility, protection, and performance we need to operate high-density GPU clusters securely, while maintaining hyperscale throughput and cost efficiency."
Where to learn more
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