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.

One Security Fabric. Every Layer of the AI Factory Protected.

From the network perimeter to inside each GPU server, five integrated security layers that catch what each other misses and respond as one. Managed centrally via a single control plane with no fragmentation.

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.

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.

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.

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.

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.

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.

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.

Governance

• 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

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.

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

Your AI Investment Deserves Security Built for AI

Whether you’re designing your first AI factory or securing an existing data center, we’ll help you get the architecture right from the start.