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 (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 cybersecurity 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, and insecure outputs, along with previously unknown attack vectors that target the core behavior of modern AI applications and can cause incalculable business disruption.

Key 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: AI infrastructure faces emerging threats like data poisoning, model theft, inference attacks, and AI-specific exploits that can manipulate the training process and output of models.
• Open ecosystems: Risks introduced by AI developers pulling code, containers, and models from public repositories.
• Significant business losses: One breach can wipe out investments and compromise intellectual property.
• AI Supply Chain failures: A hyperconnected environment introduces a much broader threat surface.
• AI applications require maximum GPU resources: Security measures must not impact AI performance.
• Compromised developers: Insider threats pose significant risks to AI factories and data centers.

Strategic Approach for Building Secure AI Systems

As organizations look to maximize the business value of AI, a strategic risk-centric approach is required. The AI Threat Triangle helps visualize three core AI domains for AI cyber security risk and business impact:

• AI Applications: Threats targeting AI models primarily through prompt injection.
• AI Responsible use and Governance: Ensuring safe, reliable behavior aligned with organizational and regulatory requirements.
• AI Infrastructure: Securing the foundational layer to prevent loss of access or compromise.

Check Point AI Data Center Security

To protect enterprises from attacks, it’s essential to secure AI data centers before they are operational. This includes security-by-design and extensive pre-deployment validation testing. The Check Point approach is a unique, holistic layered AI Security ecosystem built on a defense-in-depth approach that includes:

1. AI Native Application Protection: API-based security for AI applications.
2. Perimeter Protection: Secured entry points to AI data centers.
3. Host Security: Firewall, protecting servers (DGX) and management traffic.
4. AI-Hardware Protection: Monitoring GPU and host memory behavior for early detection of malicious activity.
5. Workload and Container Protection: Securing traffic inside Kubernetes environments.

AI Responsible Use & Governance

Without robust governance, risks remain unmanaged. Key governance and regulatory elements include:

• External AI regulations and data residency laws (EU AI Act, GDPR).
• Internal AI governance frameworks.
• Supply-chain governance to mitigate third-party risks.

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 are highly exposed. Check Point’s AI Factory & Data Center Security architecture shifts from traditional perimeter defense to model-centric protection, safeguarding AI investments and positioning organizations to scale into the next era of intelligent infrastructure.

About Check Point

Check Point Software Technologies Ltd. is a global cybersecurity leader protecting more than 100,000 organizations worldwide. Its mission is to secure enterprises' AI transformation with a unified security architecture that continuously adapts to evolving threats.