AI is not a typical cloud workload. It is high-value and high-risk. The compute footprint is massive, the data is often proprietary or sensitive and the models are intellectual property. To give you an idea:
If the pipeline is compromised, attackers can poison data, inject logic into model weights or steal credentials.
This new generation of enterprise AI demands a security model purpose-built for:
Yet most organisations continue to deploy these sensitive workloads on generic cloud infrastructure that was never designed to protect AI at scale. Many enterprise leaders face a critical dilemma: How do you scale your AI roadmap without introducing unacceptable levels of risk?
Now, enterprises are rethinking how they secure AI workloads and reallocating budgets accordingly. According to the Thales 2025 Global Cloud Security Study, 52% of organisations are prioritising AI security investments over other security needs. For many CISOs, protecting AI models and pipelines is now as critical as securing cloud infrastructure. The study also found that 64% of organisations ranked cloud security among their top five priorities (17% ranked it No.1)
Yet despite rising urgency, gaps remain. Only 8% of organisations report encrypting at least 80% of their cloud data, even though 85% acknowledge that at least 40% of their cloud workloads involve sensitive data.
In response, enterprises using AI are deploying their workloads on secure clouds that offer private and hybrid deployments as well. According to the GTT Study, 56% of respondents cited security as their top reason for moving AI workloads into private clouds, while 51% said compliance and regulatory demands are a primary driver.
The pressure on enterprises is only growing. Threats are getting more sophisticated, and regulations are becoming demanding. To succeed, enterprises must shift their mindset from “cloud security” to “AI cloud security by design”.
Here’s What That Requires:
At NexGen Cloud, we understand what enterprises need to build and scale AI workloads securely. That’s why we offer a secure cloud:
Enterprises can run their AI workloads in isolated environments with dedicated hardware. This ensures full control over compute resources, eliminates noisy neighbours and removes the risks of resource sharing with external tenants.
All data and processing can be confined to the UK or EU, helping your organisation meet GDPR, cross-border data transfer restrictions and national compliance standards. This prevents unwanted exposure to non-EU jurisdictions and reduces legal complexity.
Access can be restricted to UK-based personnel only. This enhances governance by maintaining full visibility into who accesses your data, with complete audit trails to support internal and external accountability.
We offer a transparent operational model with no foreign subprocessors or opaque third-party access. Your data, models and pipelines are deployed in environments where you retain full awareness and control over all access points.
Our infrastructure supports demanding training and inference workloads on scalable GPU Clusters for AI such as NVIDIA HGX H100 and NVIDIA HGX H200. You can also reserve capacity for the upcoming NVIDIA Blackwell GB200 NVL72/36 GPUs to future-proof your deployments.
We use NVIDIA Quantum InfiniBand interconnects and NVMe storage to deliver the bandwidth and speed required for real-time inference, fine-tuning large models and managing data-intensive workloads.
You cannot scale enterprise AI on a foundation built for general-purpose workloads. The regulations are tightening and threat actors are getting smarter. Your infrastructure must be designed for Enterprise AI from day one.
AI cloud security refers to protecting AI workloads in cloud environments through isolation, compliance controls, encryption and threat monitoring.
To ensure data privacy, control infrastructure, meet compliance requirements and protect proprietary models from exposure in shared cloud environments.
Single tenancy isolates workloads on dedicated hardware, eliminating risks from shared tenants and ensuring total control over resource access.
GDPR, AI Act and data localisation laws require strict handling, storage and auditability of AI models and datasets.
Enterprises should look for audit trails, private access controls, data residency options, and hardened infrastructure with no third-party access.
Proprietary models are valuable assets, exposing them to risks of IP theft, misuse and compliance violations, especially in regulated industries.