This applies to any enterprise deploying an internal, local AI system: a private LLM running on GPU servers, whether in their own data center or off-site with a Neocloud (GPU-as-a-Service) provider. Introducing private, local large language models and agentic applications into a data center exposes a very sophisticated and large attack surface.

No. Securing an internal AI system/LLM requires multiple layers of defense-in-depth to enforce AI-native protection across every step of the AI lifecycle: from the network perimeter and user/agent prompts, to the AI workloads themselves (training/inference), to the GPU server infrastructure.

Introducing private, local AI systems exposes an enterprise to a completely different class of vulnerability than legacy tools were built to catch. Traditional firewalls and gateways inspect ports, protocols, and known signatures. But an AI agent's most dangerous actions travel as ordinary API calls and natural-language prompts that look identical to legitimate traffic until their intent is understood. That risk exists in what is being asked, not how it's being sent, a distinction conventional network controls were never designed to make. A compromised agent can also execute in seconds what once took months of human effort, raising the stakes further. This is why AI-native security, AI runtime inspection, and AI Zero Trust are critical today.

Securing a private LLM or AI factory takes a layered approach, not a single product. Check Point's architecture secures five layers end to end: AI-native application security for prompts and API traffic, perimeter and network security at the data center edge, host-level security running on the NVIDIA BlueField DPU, hardware-accelerated AI threat detection for GPU memory forensics, and AI workload and Kubernetes container security for training and inference environments. AI Factory Firewall, the product on this page, is the host-level layer running on the BlueField DPU; the other four layers are delivered by other Check Point products, not by AI Factory Firewall itself.

The Check Point AI Factory Firewall (AIFF) is an infrastructure-native Next-Generation Firewall that runs directly inside the server chassis, as a container embedded on the NVIDIA BlueField DPU, rather than as an external perimeter box. It does not touch, inspect, or interfere with any GPU traffic, including processing within a single GPU or high-speed East-West traffic between GPUs during training or cluster synchronization. That layer is entirely bypassed, so running AIFF on the BlueField DPU introduces zero latency to the core AI training fabric.

NVIDIA BlueField DPUs act as "servers within a server." Check Point's AI Factory Firewall runs natively on the DPU as a container firewall, offloading security, networking, and storage tasks from the main server's CPU, freeing up GPU resources and improving the efficiency, security, and scalability of AI factories and Neoclouds. This isolates traffic by design: North-South storage and management traffic passes through the DPU's security mechanisms, including AIFF for traditional and AI-specific threat prevention, while East-West GPU-to-GPU data bypasses it entirely for maximum performance.

No. Check Point's security architecture is an open platform that supports many vendors across a wide range of domains, including GPU infrastructure, micro-segmentation, identity services, all major cloud providers, and more.

Check Point's AI-native security platform, built on the 2025 Lakera acquisition and subsequent innovations, provides full-stack AI runtime inspection integrated across Check Point's enterprise solutions. It supports fully air-gapped and sovereign AI deployments for regulated and sensitive markets. AI runtime inspection, AI Factory security, and policy management can all run locally with no public internet connection required.

AI Cloud Protect requires NVIDIA BlueField DPU-equipped servers (validated on NVIDIA RTX PRO servers). There is no software-only or non-DPU deployment option; the DPU is what makes the zero-overhead, inline security model possible.

Check Point's AI Factory Firewall features include securing access control, system management, administrative traffic, and access control for RAG and agentic AI: identity-based threat prevention and virtual patching (blocks unauthorized access, exploits, and zero-days targeting the AI infrastructure); runtime protection against AI-specific threats (detects prompt injection, data exfiltration, adversarial queries, and API abuse); and host-level DMA auditing via NVIDIA DOCA Argus (inspects host system memory directly, without burdening the host OS, to flag compromised or poisoned code in downloaded AI models). Performance: 40 Gbps firewall throughput per DPU, 3.2M concurrent connections, 3.3 Gbps threat prevention, 99.9% block rate, 61,000 connections per second.

Eliminates the performance-vs-security tradeoff: DPU silicon offloading delivers full-stack security at wire speed with zero GPU/CPU overhead. Enables secure multi-tenant architecture: Neocloud providers and large enterprises can isolate and manage per-tenant security policies on shared GPU infrastructure. Secures persistent AI agent working contexts: protects credentials, tool-call flows, and memory states, not just data packets, preventing lateral damage from a compromised agent. Provides access control for RAG and agentic AI, telemetry, and management, enabling safer enterprise AI adoption.