AI Cloud Security in 2025: What Enterprises Must Know Before Deploying Workloads

The Cost of Getting AI Security Wrong

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:

• Models can leak data
  Inference endpoints can unintentionally reveal sensitive training data through model inversion or membership inference attacks.
• Models are data
  Unlike software, trained AI models themselves can be exfiltrated, reverse-engineered or tampered with.
• Training pipelines are vulnerable

If the pipeline is compromised, attackers can poison data, inject logic into model weights or steal credentials.

• The supply chain is broken
  AI pipelines draw from open-source libraries, public datasets, and pre-trained models, all of which introduce backdoor or malware risks.

This new generation of enterprise AI demands a security model purpose-built for:

• Massive GPU compute
• Distributed storage
• Real-time inference
• Regulatory compliance
• Model and data sovereignty

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?

How Enterprises Are Responding to AI Cloud Security

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. 

Building AI Workloads on a Secure Cloud

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:

• Complete isolation: AI training and inference workloads should not run in shared environments where metadata leakage or side-channel attacks are possible.
• Data residency and sovereignty: Organisations operating in specific regions, for example, the EU or UK must maintain data within compliant jurisdictions to meet GDPR and other legal requirements.
• Auditability: Every AI model deployment should generate audit trails for data access, identity usage and configuration changes.
• No hidden subprocessors: Enterprises must know who processes their data, where, and why, especially under the EU AI Act.
• Scalable infrastructure: Modern AI requires enterprise-grade GPU clusters for AI, low-latency interconnects and fast data storage.

Why Choose NexGen Cloud

At NexGen Cloud, we understand what enterprises need to build and scale AI workloads securely. That’s why we offer a secure cloud:

Single-tenant deployments

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.

EU/UK hosting for full data residency

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.

Private access control and audit trails

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.

No shared tenancy or hidden subprocessors

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.

Enterprise-grade GPU clusters

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.

Low latency and high throughput

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.

Final Thoughts

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.

• Protect your data.
• Secure your models.
• Control your supply chain.
• Comply without friction.

Build Your AI on a Secure Cloud.

FAQs

What is AI cloud security?

AI cloud security refers to protecting AI workloads in cloud environments through isolation, compliance controls, encryption and threat monitoring.

Why are enterprises moving AI workloads to private clouds?

To ensure data privacy, control infrastructure, meet compliance requirements and protect proprietary models from exposure in shared cloud environments.

How does single tenancy improve AI cloud security?

Single tenancy isolates workloads on dedicated hardware, eliminating risks from shared tenants and ensuring total control over resource access.

What regulations affect AI cloud deployments in the EU/UK?

GDPR, AI Act and data localisation laws require strict handling, storage and auditability of AI models and datasets.

What security features should an AI cloud provider offer?

Enterprises should look for audit trails, private access controls, data residency options, and hardened infrastructure with no third-party access.

Why is model security critical for AI adoption?

Proprietary models are valuable assets, exposing them to risks of IP theft, misuse and compliance violations, especially in regulated industries.