Your Most Valuable Infrastructure Is Also Your Most Exposed.

AI factories with private GPU clusters, LLM training pipelines, and high-throughput inference APIs are the most valuable and vulnerable infrastructure enterprises deploy today. But, today’s legacy data center security systems were never designed to understand AI semantics, or inspect and protect this new and much larger AI attack surface.

$1T projected AI data center market by 2030.

The attack surface is scaling with it

54% have confirmed at least one AI related security incident*

Only 26% say their security architecture is ready for AI workloads*

*Cybersecurity Insiders, “2026 Securing the AI Transformation Report” cybersecurity-insiders.com

Attacks on AI Systems

• Prompt injection & manipulation
• Model theft via API enumeration
• Training data poisoning
• Adversarial input attacks

AI Misuse & Data Risk

• Sensitive data exposed in prompts
• Policy violations & toxicity
• Unintended data exfiltration
• Hallucination in critical workflows

Infrastructure Threats

• Lateral movement inside GPU clusters
• Supply chain & container compromise
• Malicious code in model weights
• Ransomware targeting training data

Secured from Day One. Not Retrofitted Later.

Every layer of the AI factory, from identity to data integrity, is designed secure from day one, not bolted on as an afterthought.

Zero Trust Everywhere

Every user, API call, agent workflow, and non-human identity authenticated, authorized, and continuously validated, including across GPU cluster east-west traffic.

AI-Native Controls

Semantic inspection that understands the intent behind prompts, not just keyword pattern matching. Built into every enforcement point.

Data & Model Integrity

Signed models, monitored training pipelines, and isolated data zones protect proprietary datasets and inference outputs from manipulation.

Red Team AI Stress Testing

AI runtime inspection, automated red teaming, and GPU memory forensics catch new AI vulnerabilities before attackers can exploit them.

AI Factory Security Blueprint: 4 Critical Insights

AI traffic and agents break the assumptions that data center security is built on.

These four insights from Check Point’s Security Blueprint show exactly where, and how to close the gap.

• Rethink the AI threat model Private AI systems will be open to internal systems by design, connected to everything, and never rest. A traditional security stack wasn’t built for that. The risk isn’t just someone breaking in. It’s the AI being turned against the business from the inside.
• Protect internal LLMs, GPU clusters, RAG pipelines AI agents, private LLMs, RAG pipelines, GPU clusters, and MCP gateways are each autonomous, semantically manipulable, and capable of cascading action. They need protection built for them, not bolted on.
• Extend zero trust to AI Never trust, always verify. Least privilege. Assume breach. The principles haven’t changed – but the attack surface has. Check Point extends AI Zero Trust enforcement natively across every layer of the AI stack, from network perimeter to prompt to AI workloads, with no gaps between enforcement points.
• Enforce defense-in-depth across five layers Secure everything. Users. Applications and agents. Network perimeter. AI factory infrastructure. Even your AI workloads and containers — with zero impact on AI performance, because host security runs on the dedicated NVIDIA BlueField DPUs, not on the GPUs.

Layer 1: AI-Native Application Security

Semantic inspection that understands the intent behind LLM prompts and API calls, not just keywords. Runs natively across Check Point firewalls, WAF, and Workforce AI Security. Embedded, not bolted on.

• API and LLM guardrail enforcement. Blocks exfiltration and policy violations
• Consistent policy across firewall, WAF, and Workforce AI Security with no fragmentation
• MCP traffic visibility for agentic AI workloads

Layer 2: Perimeter & Network Security

Maestro Hyperscale Firewall enforces Zero Trust Network Access at the AI data center edge. Separate Security Groups isolate management, private API, and public inference traffic, so traffic can never cross zones without inspection.

• 1 Tbps network security throughput
• 99.999% system resilience

Layer 3: Host-Level Security on the DPU

AI Factory Firewall runs natively on NVIDIA BlueField DPUs, embedded inside each DGX/HGX server.* Security enforcement at the hardware level, fully offloaded from CPU and GPU.

• Zero impact on token throughput or AI workload performance
• Enforcement runs inside the server before traffic reaches compute resources
• 40 Gbps firewall throughput; 3.2M concurrent connections

Layer 4: Hardware-Accelerated AI Threat Detection

NVIDIA DOCA Argus with Check Point ThreatCloud AI performs real-time GPU memory forensics, detecting supply chain compromise, reverse shell activity, and anomalous behavior without any host-side agent.

• Invisible to attackers even if the host OS is compromised
• No performance overhead. No blind spots.
• 3.3 Gbps threat prevention; 61,000 connections per second

Layer 5: AI Workload & Kubernetes Container Security

Illumio delivers micro-segmentation visibility across Kubernetes (K8s) namespaces, pods, and services. In turn, Check Point firewalls enforce policy via APIs, to block lateral movement and quarantine compromised workloads automatically.

• Open platform integrated with best-of-breed partners. No vendor lock-in
• East-west traffic visibility across training and inference namespaces

AI Zero Trust Extends to Every Agent, API, and Non-Human Identity

Agentic AI will query private LLMs autonomously, at volumes 100x to 1,000x higher than human users. Service accounts, API keys, and automated pipelines are everywhere across the AI data center. Every one is a potential attack vector if left uncontrolled.

What AI Zero Trust Covers

Check Point extends AI Zero Trust across the entire AI stack: every user, every agent, every API call, every model interaction. Access is context-aware, tied to data sensitivity and model risk classification.

• Every user and external API call
• Every agentic workflow and pipeline
• Every non-human identity and service account
• Every model interaction and inference request

Your Architecture. Your Security Management Model. Your Choice.

Three deployment models. One Check Point policy engine. A national government agency has different requirements than a Neocloud provider.

Trusted by the World’s Most Demanding Environments

“La tecnología es lo más importante, pero también se trata de las personas, y esa es otra área en la que Check Point se puso a la vanguardia”.

- Russ Trainor Vicepresidente sénior de Tecnología de la Información, Denver Broncos

“Check Point me da la tranquilidad de saber que contamos con la mejor solución de seguridad para proteger a Alkem”.

- Bijender Mishra Director de Seguridad de la Información, Alkem Laboratories

“Check Point nos brinda una solución completa que nos permite adoptar un enfoque holístico de seguridad”.

- Christopher Lee Director de Tecnología, Distrito Escolar Marple Newtown

“Con Check Point Harmony Email & Collaboration, bloqueamos miles de correos electrónicos todos los meses”.

-Piotr Baltakis Gerente de Infraestructura y Seguridad, Ciudad de Kamloops

Regulatory Realities. Compliance by Design.

The EU AI Act is in force. GDPR’s right to explanation applies today. U.S. sector mandates are tightening. Check Point’s centralized policy management and unified audit trails give security teams the traceability regulators require, as a byproduct of good architecture, not a separate workstream.

Gobernanza

• Centralized policy across training, inference, and management planes
• Unified visibility across all five security layers
• Single control plane, consistent enforcement everywhere

Traceability

• Full audit trails for API calls and model access
• Container and pipeline behavior monitoring
• End-to-end visibility from data ingestion to inference

Framework Alignment

• NIST AI RMF & Gartner AI TRiSM
• EU AI Act & GDPR
• HIPAA, PCI-DSS, ISO 42001

AI at Full Speed. Security at the Foundation.

The organizations that get AI factory security right from the start don’t just avoid breaches. They move faster, deploying AI broadly without stopping for case-by-case risk reviews every time a new use case emerges.

Confident Enterprise AI

Security and governance in place from day one means AI can be deployed broadly across departments, without case-by-case risk reviews slowing every new initiative.

Maximum GPU Performance, Always

Security offloaded to DPU hardware means zero impact on GPU throughput or token production. Your AI factory runs at full speed. Always.

Lock Down Intellectual Property

Proprietary models, training datasets, and inference outputs are among the most valuable assets your organization will ever own. Protect them accordingly.

Compliance Assured

Audit trails, governance controls, and policy enforcement aligned to global AI regulations, built into the architecture rather than retrofitted as a separate workstream.

Faster AI Deployment

Teams that trust their security foundation move faster. New AI use cases deploy in weeks instead of quarters. That speed compounds into competitive advantage.

The Numbers Behind the Architecture

30+ Years securing the world’s most demanding data centers

100K+ Organizations protected by ThreatCloud AI intelligence

$1T Projected AI data center market by 2030

Resource for Every Audience

AI Data Center & AI Factory Security Blueprint

The complete technical reference: architecture diagrams, use cases, security components, and governance alignment.

77% Have an AI Strategy. Only 26% Can Enforce It.

AI is scaling across users, applications, and autonomous agents—faster than security architecture can keep up. This gap is exposing enterprises to new risks across data, identities, cloud and hybrid environments.

Frequently Asked Questions

Who needs AI Data Center / AI Factory security?

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.

How do I secure our local private LLM and AI infrastructure?

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.

On top of these layers, AI Zero Trust extends to every user, agent, API call, and non-human identity, since agentic AI queries private LLMs at volumes 100x to 1,000x higher than human users. All of it runs through whichever deployment model that fits your environment: on-premises appliances, security management software running on your own physical/virtual appliances, or our cloud-based SaaS solution. With one consistent policy engine regardless of deployment model.

Is this simply a new ‘firewall’ appliance?

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.

Doesn’t my current security system already provide AI security? Why is it so important now?

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. This is 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.

What is an AI Network Firewall?

An AI Network Firewall is the central enforcement layer for prompt protection, AI-enabled application traffic inspection, access control for RAG and agentic AI security, telemetry, and management, enabling safer, faster enterprise AI adoption.

What exactly does the Check Point AI Factory Firewall do?

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.

How does NVIDIA’s BlueField DPU card and Check Point AI Factory Firewall work together?

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.

Does Check Point only secure NVIDIA-based AI systems?

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.