Enterprise adoption of artificial intelligence continues to accelerate across global data center environments. As AI workloads shift from pilot programs into production, AI factories’ security has become a core operational requirement. Vendors now align security platforms directly with infrastructure designs that support large-scale AI training and inference.
Check Point Software has joined Nvidia’s Enterprise AI Factory validated design, embedding runtime cybersecurity into reference architectures used for enterprise AI deployments. The integration places AI Cloud Protect directly within infrastructure built for training and operating AI models in dedicated data center environments, as organizations scale production AI workloads globally.
Check Point Added to Nvidia Enterprise AI Factory
Check Point Software has added its AI Cloud Protect product to Nvidia’s Enterprise AI Factory validated design. The move places the cybersecurity vendor inside a reference architecture designed for organizations operating AI workloads in dedicated data center environments.
The companies described the integration as focused on runtime protection for “AI factories.” Nvidia and other infrastructure providers use the term to describe purpose-built systems for training and running AI models. The validated design forms part of Nvidia’s broader portfolio of enterprise AI reference configurations.
The announcement follows increased enterprise spending on AI tooling and infrastructure. At the same time, security teams assess exposure across AI pipelines, prompts, and model behavior. The advisory referenced third-party research indicating that such threats already affect production environments.
Research Highlights Growing AI Security Exposure
The statement cited findings from Gartner, which reported that 32% of organizations experienced an AI attack involving prompt manipulation during the past year. Gartner also found that 29% of organizations faced attacks on their generative AI infrastructure.
The advisory further referenced survey data from Lakera. According to the survey, 19% of organizations described their generative AI security posture as “highly confident.” In contrast, 49% reported high concern about AI-related vulnerabilities.
These figures point to a widening gap between AI deployment speed and security preparedness across industries.
AI Cloud Protect Positioned in Validated Design
Check Point said AI Cloud Protect now forms part of the Nvidia Enterprise AI Factory validated design for AI runtime cybersecurity. The product operates as a security layer across infrastructure that hosts AI workloads.
The company also stated that its software is “validated on NVIDIA RTX PRO Servers.” It positioned the combination as a way to secure AI factories at scale. The advisory claimed there is “no negative impact to AI system performance.”
In the same statement, Check Point described AI factories as “the new class of purpose-built data centers for AI.” Vendors increasingly market AI infrastructure as integrated platforms rather than standalone components.
BlueField Integration Expands Runtime Visibility
Check Point linked its deployment to Nvidia’s BlueField platform. The company said the integration “tackles cyber threats and vulnerabilities” through real-time monitoring and isolation between AI workloads. It also said the platform provides “deep visibility and control over AI data.”
According to the advisory, AI Cloud Protect delivers real-time network and host security using Nvidia DOCA Argus telemetry. Check Point combines this telemetry with its “native AI-powered cyber security” to support runtime detection and response.
The company described a multi-layer security framework spanning infrastructure, applications, and users. It framed this model as an “AI supply chain” approach that applies policies across different parts of the AI stack.
Three Layers of Protection Across AI Operations
At the infrastructure layer, Check Point said AI Cloud Protect runs on Nvidia BlueField. The company stated that the software secures AI infrastructure “without consuming precious GPU capacity.” It repeated its claim of “zero negative performance impact.”
For the application layer, Check Point highlighted CloudGuard Web Application Firewall. The company said the product addresses threats such as “prompt injection, jailbreaking, and LLM poisoning.” It also referenced runtime protection for “LLM inputs, outputs, and all data flows-including retrieval-augmented generation (RAG) and model context protocol (MCP) servers.”
Check Point claimed a “unique data advantage from Gandalf.” It described Gandalf as “the world’s largest AI red team platform with over 80 million adversarial attack patterns.” The company linked this data to detection accuracy and reduced false positives.
At the user layer, Check Point described GenAI Protect as a governance tool. The product governs employee AI usage and prevents sensitive data leakage. It also provides visibility into AI tools used by staff and generates compliance audit trails.
Across global markets, enterprises continue to formalize AI deployment strategies. Regulators and customers now expect stronger controls around data protection, model integrity, and operational resilience. Vendors increasingly emphasize runtime monitoring, application-level protection, and employee governance as AI systems integrate deeper into business operations.
Security Embedded Into AI Factory Design
By adding AI Cloud Protect to Nvidia’s Enterprise AI Factory validated design, Check Point positions runtime security closer to enterprise AI operations. As organizations expand AI factories worldwide, this approach reflects a broader industry shift toward embedding security directly into standardized AI infrastructure.
