Securing AI factories: Why enterprise-grade cybersecurity is becoming foundational to AI adoption
As enterprises and service providers accelerate the deployment of generative and agentic AI systems, cybersecurity is emerging as a defining constraint on how quickly, and safely, AI can scale. From model training and inference infrastructure to applications and end-user interactions, the AI pipeline is expanding the enterprise attack surface in ways traditional security architectures were never designed to handle.
Industry data already points to this shift. A recent study cited by Gartner indicates that nearly one-third of organisations have experienced attacks involving prompt manipulation, while close to 30% have faced direct attacks on their generative AI infrastructure over the past year. At the same time, confidence remains low: research from Lakera shows that fewer than one in five organisations describe their GenAI security posture as highly mature.
These trends underline a growing consensus among security leaders that AI adoption requires rethinking cyber defence—not as an add-on, but as a core architectural layer.
AI infrastructure meets enterprise security expectations
AI workloads are increasingly being deployed in purpose-built environments often described as “AI factories”—data centre architectures optimised for large-scale model training and inference. While these environments deliver massive compute capability, they also introduce new risks related to workload isolation, data visibility and runtime manipulation.
Against this backdrop, Check Point Software has aligned its AI security capabilities with AI infrastructure built on NVIDIA platforms. The objective is to bring security controls closer to where AI workloads actually run, without degrading performance—an issue that has historically limited the effectiveness of security tools in high-performance environments.
Check Point’s AI Cloud Protect has been validated as part of NVIDIA’s Enterprise AI Factory reference architecture, designed to secure AI runtime environments operating at scale. The approach reflects a broader industry shift toward embedding security directly into AI infrastructure rather than relying solely on perimeter-based controls.
Securing the AI pipeline, not just the model
One of the defining challenges of enterprise AI security is that threats no longer target only applications or endpoints. Attacks now span infrastructure, models, data pipelines, APIs and user interactions—often exploiting the connections between them.
At the infrastructure level, integrating security with NVIDIA’s BlueField platform enables real-time monitoring and isolation across AI workloads, providing visibility into traffic flows and data movement without consuming GPU resources needed for AI processing. This reflects an emerging best practice: decoupling security enforcement from compute-intensive AI tasks while maintaining fine-grained control.
At the application layer, AI introduces new attack vectors such as prompt injection, model jailbreaking and poisoning of retrieval-augmented generation (RAG) pipelines. These risks require runtime protection mechanisms that can inspect inputs, outputs and contextual data flows in real time—capabilities that go beyond traditional web application firewalls.
At the user layer, enterprises face growing concerns around uncontrolled employee use of generative AI tools. Shadow AI adoption can lead to inadvertent data leakage, regulatory exposure and loss of intellectual property, making visibility and governance critical components of AI security strategies.
Network-layer visibility for agentic AI
As agentic AI systems begin to autonomously interact with enterprise applications and services, network-level controls are becoming increasingly relevant. Detecting and governing traffic associated with model context protocols (MCP), AI agents and unmanaged GenAI tools allows organisations to extend familiar security concepts—such as application control and policy enforcement—into AI-driven workflows.
This network-centric perspective is particularly important for large enterprises, where AI tools are often introduced incrementally across departments, cloud environments and geographies.
A broader shift in AI risk management
Taken together, these developments signal a shift in how organisations are thinking about AI security. Rather than treating AI risks as experimental or future concerns, enterprises are increasingly recognising them as immediate operational and governance challenges.
As AI factories, agentic systems and large language models become embedded in core business processes, security leaders are under pressure to deliver protection that scales with performance, preserves data integrity and enables innovation without introducing unacceptable risk.
The convergence of AI infrastructure and enterprise-grade cybersecurity reflects a broader reality: in the next phase of AI adoption, trust, resilience and control may matter as much as model accuracy or compute power.