If you're leading AI at an enterprise level such as training models on sensitive datasets, deploying them at scale or integrating them into business functions, you need to be concerned. Not just about performance or cost but about the serious, rising risks in AI cloud security.
The attack surface has grown with the rise of AI. These models touch regulated personal data, proprietary algorithms and decision-critical outputs. And they're being run across hybrid clouds, third-party APIs, open-source dependencies and large-scale GPU clusters for AI that may not even be in your jurisdiction.
Even more concerning is if you're operating in the EU, GDPR and the EU AI Act demand far more than just security checklists. There are massive fines and reputation damage that took you years to build. Continue reading as we discuss the top 5 challenges enterprises are facing in AI Cloud Security.
You might already be running massive training pipelines using cloud GPUs for AI. But here’s what many enterprises overlook: the data feeding these models is often the most vulnerable part of your AI infrastructure.
Not adhering to the data laws of GDPR can lead to hefty fines of up to €20 million or 4% of your annual global turnover (whichever is higher). Even top companies like Meta were fined €1.2 billion by Ireland due to the transfer of data from the European Union to the United States without adequate privacy protections.
If your model has been fine-tuned using internal IP or sensitive customer data, its value goes far beyond just weights and tokens, it’s a part of your business strategy. So it’s worth asking: how secure is it,?
Multitenancy is not inherently insecure but not all implementations are equal. Some cloud setups don’t prioritise isolation or workload-level security, leaving room for:
Model theft via unsecured inference APIs
Side-channel leakage from neighbouring tenants
Poor container isolation that risks exposure of weights or checkpoints
And while shared infrastructure can make sense for many use cases, not every workload should be treated the same. If you're in a regulated industry, deploying a hybrid cloud strategy, or investing months in fine-tuning, you’ll definitely want a secure cloud.
You might already be familiar with GDPR but the future will bring an even tighter net around AI systems with new EU mandates and global pressure for AI transparency and accountability.
Hence, hosting or training models on infrastructure outside the EU or without clear jurisdictional controls can result in non-compliance. Your AI system might be performant but if you can’t prove compliance by design and secure hosting, be ready to risk multi-million euro fines.
Security and compliance should never be thought of after deployment. They need to be part of your cloud strategy from the initial training run.
MLOps helps in the faster deployment of AI. But it also introduces new security blind spots, especially when integrating open-source tools, containerised environments and automation.
Your DevOps team might be confident in their CI/CD processes but how much visibility do you have into the actual ML components being used? Your weakest link might be a single pip install in a training script.
One of the most insidious threats isn’t malicious actors but your own internal teams deploying unverified AI tools without oversight.
With platforms like OpenAI, Claude, Gemini and open-source LLMs widely accessible, non-technical teams can build or deploy AI without IT oversight. This creates massive data exposure risks, fragmented security policies and regulatory blind spots.
If you don’t have strong identity and access management (IAM), RBAC, audit logging and model registry controls, you’re flying blind. A model fine-tuned on internal data and deployed to an insecure endpoint could trigger both a breach and non-compliance.
To protect sensitive AI workloads and meet strict regulations, enterprises must build on infrastructure that prioritises security, sovereignty and performance
Every inference run, every training job and every open-source dependency is a potential breach point if not handled properly.
Enterprises can’t afford to be reactive. As EU regulations tighten and AI cloud security threats become more relevant, enterprises must embed security and compliance at every stage of their AI workflows.
So ask yourself:
If the answer isn’t a confident yes, it’s time to build and deploy your AI workloads on a secure cloud. And NexGen Cloud delivers exactly that.
Data leakage during training is a major risk, especially when using sensitive datasets on shared or non-secured infrastructure.
EU/UK hosting ensures compliance with GDPR and AI regulations by keeping all processing and data within approved legal jurisdictions.
Yes, shared GPUs can expose your models to theft via side-channel attacks or misconfigurations if not properly isolated.
Shadow AI refers to unsanctioned model use, risking data exposure, compliance failures, and a lack of IT governance visibility.
Use single-tenant GPUs, encrypt model weights, and deploy on clouds that guarantee no shared access or subprocessors.
NexGen Cloud offers a secure cloud deployment with EU/JK-based hosting, single-tenant GPU infrastructure with full access control, audit logs and enterprise-grade performance for compliant AI operations.