Executive Guide | Securing the AI Data Center & AI Factory

Securing the AI Data Center & AI Factory Guide for CTO, CISO, CAIO

Executive Summary

The enterprise adoption of private AI and LLM (Large Language Model) infrastructures introduces a new breed of risks. Unlike traditional IT workloads, AI factories and data centers manage sensitive training data, powerful GPU clusters, distributed inference services, and high-throughput pipelines. This new and much broader attack surface increases threats to data, intellectual property, proprietary AI models, and end-users. Another segment building their own AI factories are Neocloud providers, who deliver GPU-as-a-Service, building hyperscale AI factories powered by NVIDIA and other leading GPU platforms to give enterprises on-demand, high-performance compute for training and inference.

Deploying AI capabilities and private LLMs (Large Language Models) without embedding additional security increases exposure to poisoning, data leakage, and governance failures. To ensure system resilience, AI data centers must be secured end-to-end — from the fabric and GPU clusters to Kubernetes workloads, along with API-driven inference workloads and services. Check Point enables organizations to confidently adopt and scale AI by addressing both traditional IT threats and new vulnerabilities introduced by AI-driven environments. Check Point delivers embedded cyber security by design to cover all sensitive blocks of AI data centers and private LLMs.

An AI Arms Race

The race to build AI is accelerating. Enterprises are investing billions in AI factories that power the creation and development of AI capabilities. More than half of enterprise networks now use AI tools, making them prime targets for cyber attacks. Check Point data shows that 1 in every 80 GenAI prompts exposes sensitive data. Meanwhile, a recent Gartner report found that 32% of organizations experienced an AI attack involving prompt manipulation, and 29% faced attacks on their GenAI infrastructure in the past year. Protecting the entire AI pipeline, from development to production, has become an urgent imperative. As organizations scale their AI infrastructure, they need comprehensive security solutions that won’t come at the cost of AI server performance.

• AI Factory Overview
• AI Security Report 2025
• Gartner AI Attack Report

AI Cyber Security Threats and Risks

AI security risks are compounded by the huge investment costs required by AI infrastructure and AI applications, which in turn amplifies and accelerates the threat landscape. The pace of change in attackers’ capabilities is reflected by industry establishments, such as OWASP, who recently published the Top 10 LLM threats. The new AI threat landscape includes the following risks and attack methods:

• Prompt injection and jailbreaks: Bypass controls or trigger unintended behavior.
• Model poisoning: Corrupted training data that degrades accuracy or embeds backdoors.
• Data leakage: Model prompt responses reveal confidential or regulated information.
• AI-driven cyber threats: Emerging threats like data poisoning, model theft, inference attacks.
• Open ecosystems: Risks from pulling code and models from public repositories.
• Significant business losses: Training LLMs involves processing massive volumes of sensitive data; breaches can wipe out investments and compromise intellectual property.
• AI Supply Chain failures: Introduces a much broader threat surface than traditional IT data centers.
• Compromised developers: Insider threat or credential theft poses significant risks.

Strategic Approach for Building Secure AI Systems

A strategic risk-centric approach is required to help communicate where key risks exist and how to mitigate them. Check Point developed an AI threat triangle to visualize core AI domains for AI cyber security risk and business impact:

1. AI Applications: External threats targeting AI models mainly through prompt injection.
2. AI Responsible Use and Governance: Focused on ethical operation of AI systems.
3. AI Infrastructure: The foundational layer; if compromised, all other layers are impacted.

Check Point AI Data Center Security

Securing AI data centers after they are running is prohibitively expensive. Therefore, ensuring security-by-design and extensive pre-deployment validation testing is critical. The Check Point approach to protect AI factories and data centers includes:

1. AI Native Application Protection: API-based security of AI applications.
2. Perimeter Protection: External access control anchored by zero-trust.
3. Host Security: Firewall running on NVIDIA DPU, securing management traffic.
4. AI-Hardware Protection: Monitors GPU and host memory in real-time.
5. Workload and Container Protection: Secure East-West traffic within Kubernetes environments.

Securing the AI Factory & Data Center

• AI Applications Protection: AI-native security is key to success. Runtime protection against threats like prompt injection and data leakage.
• AI Infrastructure Security: Security capabilities tailored for AI applications, ensuring robust perimeter security and governance control.

The Bottom Line

AI factories and AI-powered data centers are rapidly becoming the digital core of the modern enterprise. Without security purpose-built for AI, these environments become the most exposed. Check Point’s AI Factory & Data Center Security architecture represents a shift from traditional perimeter defense to model-centric protection, ensuring that organizations can confidently scale into the next era of intelligent infrastructure.

About Check Point

Check Point Software Technologies Ltd. is a global cyber security leader protecting more than 100,000 organizations worldwide. Built on a prevention-first approach and an open ecosystem architecture, Check Point helps organizations reduce risk, simplify operations, and innovate with confidence.