AI is not an innovation limited to tech labs and research teams anymore. Now, enterprises are adopting AI at a large scale into business processes that touch customers, employees and critical systems. According to industry research, 85% of enterprises now use cloud infrastructure to run AI and ML workloads and over 60% of corporate data is stored in the cloud to fuel AI analytics.
But with scale comes a harsh reality that not many are aware of. AI offers security risks that can go far beyond conventional IT applications. In 2025, 73% of enterprises reported at least one AI-related security incident, with average breach costs of nearly $4.8 million per incident. To give you a better idea, attacks targeting AI systems have become faster. Research found that most enterprise AI systems could be compromised in just 16 minutes with critical flaws uncovered in 100% of systems analysed.
In this blog, we discuss how enterprises handle security when deploying AI workloads at scale and how NexGen Cloud is helping organisations balance performance while being secure.
The Security Risks of Deploying Sensitive AI Workloads at Scale
AI workloads are different from traditional enterprise applications. They consume more data, are intensive and interconnected across teams, tools and environments. And the way enterprises scale AI initiatives, the attack surface definitely expands at scale too.
Sensitive Data Exposure
AI models often rely on vast amounts of sensitive data: personally identifiable information (PII), financial records, proprietary intellectual property, healthcare data or customer interaction logs. Unlike traditional applications that may access data intermittently, AI systems ingest, process and retain data across training, fine-tuning and inference stages.
For instance, according to Netskope Threat Labs’ Cloud and Threat Report: 2026, tens of thousands of incidents involving sensitive data shared with generative AI tools have been recorded monthly, with an average of 223 such events per organisation per month. These violations often involve regulated personal, financial or healthcare data.
Model-Specific Security Risks
AI introduces risks that do not exist in conventional software systems. Model poisoning, data contamination, adversarial inputs and malicious model weights can compromise outcomes in subtle but severe ways. A compromised model may still “work” while producing biased, manipulated or insecure outputs. In healthcare alone, research has shown that attackers may be able to compromise AI models with a small number of poisoned samples with alarming success rates. This makes detection far more difficult than traditional breaches.
Infrastructure and GPU Security
Modern AI workloads rely on powerful GPU clusters and distributed systems. In shared or poorly isolated environments, vulnerabilities in virtualisation layers, networking or storage can expose workloads to neighbouring tenants. For enterprises running sensitive AI workloads, even the perception of shared risk can be unacceptable.
The Importance of Data Sovereignty and Regulations
Security decisions around AI are now shaped by regulation. Governments and regulators worldwide recognise that AI systems can impact individual rights, economic stability and national security.
Data Sovereignty as a Strategic Requirement
Data sovereignty refers to the requirement that data remains subject to the laws and governance structures of the country or region where it is collected. For AI workloads, this matters more than ever.
Training and inference often involve moving large datasets across regions for performance or cost reasons. However, unrestricted cross-border data movement is no longer acceptable for many enterprises operating in regulated industries or public sectors.
Enterprises must now answer difficult questions:
- Where is our AI data stored?
- Where is it processed?
- Who has access at each stage?
- Under which legal jurisdiction does it fall?
In Europe, for instance:
The General Data Protection Regulation (GDPR) remains one of the strictest data privacy frameworks globally. When AI systems ingest, process or generate personal data, organisations must:
- Document data lineage and purpose of processing
- Ensure adequate protection and governance
- Enable rights such as access, correction and deletion
The EU AI Act
The upcoming EU AI Act is one of the first comprehensive, risk-based regulatory frameworks for AI systems. It classifies AI applications by risk level, imposing additional requirements on high-risk systems covering:
- Data quality, documentation and traceability
- Human oversight and robustness
- Cybersecurity and incident reporting
Why Traditional Infrastructure Falls Short for Secure AI
Many enterprises initially attempt to deploy AI workloads on existing cloud or on-premise infrastructure. Over time, limitations become clear.
Multi-tenant environments and shared risk
General-purpose cloud platforms are optimised for flexibility and cost efficiency, not necessarily for high-risk AI workloads. Multi-tenant GPU environments can introduce concerns around noisy neighbours, side-channel attacks and insufficient isolation for sensitive data.
While some of these platforms may meet baseline compliance requirements, they often lack the control enterprises need for advanced AI governance. However, cloud GPU platforms like Hyperstack address this gap by offering both public and secure private cloud environments, allowing organisations to choose the deployment model that best aligns with their security, compliance and project needs.
On-premise constraints
On-premise infrastructure offers control but comes with some trade-offs. GPU procurement cycles are slow, capital costs are high and scaling infrastructure to meet AI demand can take months or years. Security teams must also manage physical security, patching, monitoring and compliance independently. As these AI workloads grow in scale and urgency, these limitations can slow innovation and increase operational risk.
Why Enterprises Choose Secure Cloud Infrastructure for AI Workloads
To balance performance with security, enterprises are turning to secure, purpose-built AI cloud infrastructure. For instance, enterprises choose clouds that are:
Secure by Design
Secure AI cloud platforms are built with isolation, encryption and governance as major principles rather than add-ons. Single-tenant deployments eliminate shared risk to ensure that each enterprise’s data, models and workloads remain fully isolated.
Private networking, role-based access control and detailed audit logs allow security teams to maintain visibility and control without sacrificing agility.
Scalable without Compromise
Secure cloud infrastructure enables enterprises to scale GPU resources on demand while maintaining consistent security postures. Training large models, running distributed workloads and supporting real-time inference can all happen within controlled environments.
How NexGen Cloud Enables Secure Enterprise AI at Scale
Investing in next-generation AI infrastructure is no longer about short-term gains. Now, enterprises need to build a foundation that can scale with AI ambition, deliver consistent results and meet the highest standards of security and compliance. This is why many organisations choose NexGen Cloud’s Secure Cloud AI infrastructure:
- Built for performance and control: NexGen Cloud is designed from the ground up for AI workloads that require both raw compute performance and enterprise-grade control. Enterprises gain access to dedicated NVIDIA GPU clusters, including NVIDIA HGX H100, NVIDIA HGX H200 and upcoming NVIDIA Blackwell GB200 NVL72/36 systems. These GPU clusters support large-scale training and inference for faster model development, efficient scaling and predictable performance in production.
- High-performance networking and storage: Performance at scale depends on more than GPUs alone. NexGen Cloud integrates NVIDIA Quantum InfiniBand networking with NVMe-based storage to deliver ultra-low latency and high-throughput data movement across the AI stack. This ensures GPUs remain fully utilised, distributed training runs efficiently and latency-sensitive inference workloads meet strict enterprise requirements.
- Security and compliance as first-class concerns: Security is embedded into the NexGen Cloud by design. Single-tenant deployments provide complete data isolation, significantly reducing exposure risk while maintaining cloud flexibility. Private access controls, detailed audit trails and enterprise-grade monitoring support internal governance processes and external regulatory obligations, including GDPR and specific compliance frameworks.
- Data sovereignty and regional assurance: For enterprises operating in Europe and the UK, data residency is non-negotiable. NexGen Cloud offers EU- and UK-based hosting under domestic jurisdiction, ensuring sensitive AI data remains within compliant legal boundaries. This helps organisations in finance, healthcare, government and other regulated industries to deploy AI workloads confidently without compromising sovereignty requirements.
Speed without Shortcuts
NexGen Cloud enables enterprises to deploy secure AI infrastructure rapidly in a public cloud environment, avoiding the long lead times and capital expenditure of on-premise builds. Teams can experiment, scale and move to production faster without cutting corners on security or compliance.
FAQs
Why is securing AI workloads more complex than traditional IT workloads?
AI workloads handle large volumes of sensitive data, rely on distributed GPU infrastructure and introduce model-specific risks like poisoning and inference attacks, significantly expanding the enterprise security attack surface.
How does data sovereignty impact enterprise AI deployments?
Data sovereignty requires AI data to remain within specific legal jurisdictions. Enterprises must control where data is stored, processed and accessed to comply with regulations like GDPR and regional AI laws.
What role does the EU AI Act play in AI security?
The EU AI Act introduces risk-based AI regulation, requiring stronger governance, traceability, cybersecurity controls and human oversight for high-risk AI systems used in enterprise environments.
Why are secure cloud environments preferred for enterprise AI workloads?
Secure cloud platforms provide isolated infrastructure, on-demand GPU scalability, advanced access controls and compliance support, enabling enterprises to scale AI workloads without sacrificing security or performance.
October 1, 2024
Updated on 10 Feb 2026
