Executive Guide | Securing the AI Data Center & AI Factory

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 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. While AI provides amazing productivity benefits, AI systems face unprecedented security challenges. Protecting the entire AI pipeline, from development to production, has become an urgent imperative.

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 list includes:

• Prompt injections
• Data leakage
• Model manipulation
• Insecure outputs

The new AI threat landscape includes the following risks and attack methods:

• Prompt injection and jailbreaks: Manipulative inputs that 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: This includes attacks like data poisoning, model theft, inference attacks, and AI-specific exploits that can manipulate the training process and output of models.
• Open ecosystems: AI developers pulling code, containers, and models from public repositories create additional risks like model poisoning, data exfiltration, and malicious workloads hiding in downloaded models.

Strategic Approach for Building Secure AI Systems

As organizations look to maximize the business value of AI, a strategic risk-centric approach is required. A clear understanding of the likelihood and impact of attacks against AI systems means a clearer protection-first approach. To help communicate where key risks exist and how to mitigate them, Check Point developed an AI threat triangle.

The AI Threat Triangle

• AI Applications: Targeted mostly by external threats through prompt injection.
• AI Responsible use and Governance: Focused on ensuring safe, reliable behavior in line with organizational and regulatory requirements.
• AI Infrastructure: The foundational layer; if compromised, all other layers are impacted.

Check Point AI Data Center Security

Check Point's approach to protecting AI factories and data centers is unique and holistic, based on a layered AI Security ecosystem built on a defense-in-depth approach. The conceptual technologies stack includes:

1. AI Native Application Protection (LLM): API based security of AI applications.
2. Perimeter Protection: External access control zone secured by zero-trust.
3. Host Security: Firewall running close to the AI workloads, protecting management traffic.
4. AI-Hardware Protection: Monitoring GPU and hosts' memory behavior in real-time to detect anomalous access patterns.
5. Workload and Container Protection: Securing East-West traffic inside Kubernetes environments.

Securing the AI Factory & Data Center

The unique nature of AI applications means that AI-native security is key to success. This includes:

• Check Point AI LLM Security and API Security.
• AI-native runtime protection against prompt injection and data leakage.
• Real-time API protection and auto-discovery to prevent advanced threats, including OWASP Top-10 and zero-day attacks.
• Model Context Protocol (MCP) for traffic visibility.

AI Responsible Use and Governance

Without robust governance, organizations lack visibility and control mechanisms to manage unique risks. Therefore, applying a security-by-design approach assures that governance is a high priority for AI data center architects. Key governance elements to consider include:

• External AI regulations and data residency laws.
• International AI frameworks ensuring data lineage and quality.
• Internal governance frameworks to guarantee least-privilege access to data and training infrastructure.