October 1, 2024
Updated on 21 Jul 2025
Table of contents
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.
1. Data Leakage in AI Model Training
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.
What’s the Risk?
- Training datasets include PII, financial records, customer interactions, and proprietary product data.
- If stored improperly, accessed by shared VMs, or cached across multiple clusters, this data can leak.
- Worse, language models can unintentionally memorise and regurgitate sensitive data.
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.
2. Model Theft and IP Risk in Multi-Tenant GPU Clouds
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:
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Model theft via unsecured inference APIs
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Side-channel leakage from neighbouring tenants
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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.
3. Insufficient Compliance with Evolving AI Regulations
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.
The key regulations will include:
- EU AI Act: High-risk systems must prove data provenance, bias mitigation and explainability (Article 6). Cloud providers must offer tools for risk monitoring and audit logging.
- NIS2 Directive: Requires stricter cybersecurity measures for "essential" services, including many AI-powered systems in healthcare, finance and public sectors.
- AI Export Control: There will be limits on where and how AI models and datasets can be moved.
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.
4. Insecure MLOps Pipelines and Supply Chain Vulnerabilities
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.
What’s the Risk?
- Compromised pre-trained models from public hubs.
- Malicious ML libraries that exfiltrate data at runtime.
- Insufficient secrets management in orchestration tools like Kubeflow, MLflow, or Airflow.
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.
5. Shadow AI and Poor Access Governance
One of the most insidious threats isn’t malicious actors but your own internal teams deploying unverified AI tools without oversight.
What is Shadow AI?
- Employees are using third-party AI tools, uploading sensitive data to consumer-grade LLMs or running unsanctioned fine-tuning jobs without the approval of IT department or the governing bodies.
- Lack of visibility over who’s using what AI model, on what data and where it’s being hosted.
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.
Build on a Secure Cloud
To protect sensitive AI workloads and meet strict regulations, enterprises must build on infrastructure that prioritises security, sovereignty and performance
- Single-tenant deployments
Run your AI workloads in isolated environments with dedicated hardware to ensure full control and no resource sharing with external tenants. - EU/UK hosting for full data residency
Keep all data and processing within the UK or EU to comply with GDPR and cross-border data transfer laws. Hence, you can avoid exposure to non-EU jurisdictions and reduce legal risk. - Private access control and audit trails
You can lock access to UK-based personnel only. This helps enterprises maintain full visibility with traceable logs for accountability. - No shared tenancy or hidden subprocessors
Enterprises deploying AI at scale must remove the risk of foreign subprocessors and opaque third parties. Your models, data and pipelines run in an environment where you know exactly who has access and who doesn’t. - Enterprise-grade GPU clusters
Train and deploy your models on NVIDIA HGX H100, NVIDIA HGX H200 and reserve capacity for the upcoming NVIDIA Blackwell GB200 NVL72/36. - Low latency and high throughput
NVIDIA Quantum InfiniBand and NVMe storage deliver the bandwidth and speed required for real-time inference, fine-tuning large models and managing massive datasets.
Conclusion
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:
- Is your AI infrastructure really secure?
- Can you prove compliance today?
- And are you sure your most valuable IP isn’t already exposed?
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.
FAQs
What is the biggest AI cloud security risk in 2025?
Data leakage during training is a major risk, especially when using sensitive datasets on shared or non-secured infrastructure.
Why is EU data residency important for AI workloads?
EU/UK hosting ensures compliance with GDPR and AI regulations by keeping all processing and data within approved legal jurisdictions.
Can shared GPU infrastructure compromise my AI models?
Yes, shared GPUs can expose your models to theft via side-channel attacks or misconfigurations if not properly isolated.
What is Shadow AI and why is it dangerous?
Shadow AI refers to unsanctioned model use, risking data exposure, compliance failures, and a lack of IT governance visibility.
How can I protect my AI models from theft?
Use single-tenant GPUs, encrypt model weights, and deploy on clouds that guarantee no shared access or subprocessors.
Why choose NexGen Cloud for secure AI deployment?
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.
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