Response Thriving with AI Cloud: Balancing Opportunities and Risks

Introduction

Artificial intelligence (AI) is transforming industries at an incredible pace and scale. From generative models that create convincing text and images, to predictive analytics that drive business decisions, AI is now central to innovation across sectors. 

Behind the scenes, cloud infrastructure, also known as AI as a Service (AIaaS) or AI cloud, can provide significant benefits over on-premises AI.  These are the result of a confluence of technical, commercial, and regulatory factors.

However, as organisations embrace cloud-based AI, they also encounter new and evolved operational, legal and strategic challenges. For lawyers and business leaders, understanding both the drivers and the risks of this space is essential – not only for compliance, but also for competitive advantage.

The case for choosing cloud AI

Scalability and elasticity

AI workloads, including those involving large language models and deep neural networks, demand vast computational resources, and can involve thousands of high-performance processors running for days or weeks, consuming vast amounts of memory and storage. The cloud offers elastic scalability: organisations can instantly provision the resources they need, then scale down when demand drops. This flexibility is difficult to replicate on-premises without substantial up-front and ongoing investment.

For example, a financial institution developing a fraud detection model may need to process petabytes of transaction data. In the cloud, it can spin up thousands of Graphics Processing Units (GPUs) for training, then release those resources once the job is complete, paying only for what it uses.

Access to specialised hardware

The latest advances in AI are powered by specialised hardware accelerators, such as NVIDIA A100/H100 GPUs, Google Tensor Processing Units (TPUs), and custom Application-Specific Integrated Circuits (ASICs). These accelerators are not only expensive but also require specialised expertise to operate and maintain. Cloud providers offer these resources “as a service”, making them accessible to a range of clients. This democratisation of advanced hardware can level the playing field, enabling start-ups and small and medium-sized enterprises (SMEs) to compete with established giants in AI innovation.

Deployment, operational efficiency and innovation

Cloud platforms come with integrated AI frameworks, pre-built Application Programming Interfaces (APIs), software development kits (SDKs), and machine learning operations (MLOps) pipelines that streamline the development lifecycle. Organisations can move from prototype to production in weeks rather than months. 

For instance, shortly following a decision to do so, a healthcare provider can begin to leverage cloud-based natural language processing APIs to extract insights from clinical notes. This approach side-steps the time-consuming alternative of building and maintaining the underlying models on premises.

In addition, the combination of AI and cloud capabilities enables automation and streamlining of repetitive tasks, which, in turn, can increase operational efficiency, minimise human error and reduce time spent on manual interventions. This means that internal teams can focus on innovation and strategic initiatives, rather than infrastructure maintenance.

Cost efficiency and flexibility

Unlike traditional on-premises solutions, which generally require upfront investment and ongoing maintenance costs, AI resources are available on a pay-as-you-go basis. This cost efficiency supports experimentation, prototyping, and scaling without long-term lock-in to physical assets. It also makes AI accessible to a broader range of organisations.

Data management and collaboration

AI thrives on data, and the cloud can centralise data access, organisation and storage as well as enable global collaboration. Teams distributed across continents can access, annotate and analyse vast amounts of data in real time, enabling effective decision-making. Cloud platforms can also facilitate better data governance and effective data monitoring – essential for ensuring explainability, auditability and compliance with data protection laws.

Security and reliability

Major cloud providers invest significantly in security, offering features such as end-to-end encryption and zero-trust architecture. They can also provide high levels of reliability, with redundancy, failover, and global availability built into their platforms.

Regulatory and compliance features

Cloud providers offer recognised security attestations and authorisations (eg, ISO 27001, SOC 2, FedRAMP) and extensive compliance documentation. These can support an organisation’s compliance programme, but do not by themselves ensure regulatory compliance or reduce the organisation’s responsibilities under frameworks such as GDPR or HIPAA.

Key risks of cloud-based AI

Despite these advantages, the cloud-centric AI model introduces a range of operational, legal and strategic risks. These must be carefully managed to avoid regulatory scrutiny, reputational harm and operational disruption.

Integration and interoperability

Although cloud AI platforms are intended to integrate with various tools, allowing seamless workflows and supporting complex solutions, some organisations’ legacy systems and old applications may not integrate easily with the cloud. Any organisation considering AIaaS should as a first step address integration and interoperability challenges and create a cloud migration strategy or an AI adoption framework.

Vendor lock-in

Cloud AI platforms often bundle compute, storage, frameworks and APIs in tightly integrated ecosystems. Once an organisation builds its models and workflows around a particular provider (eg, Amazon Web Services (AWS), Microsoft Azure AI, or Google Cloud Vertex AI), switching to another platform can be costly and technically challenging. This “lock-in” exposes organisations to pricing changes, licensing shifts, or even service discontinuations, with limited leverage to negotiate or exit.

From a legal perspective, this raises important questions about – eg, contractual terms, exit strategies and data and model portability. Lawyers advising on cloud AI contracts should scrutinise provisions relating to data export, interoperability and the consequences of termination.

Data sovereignty and jurisdiction

Leakage of sensitive data from the data sets on which AI models are trained is a very real concern. This is particularly so because AI often involves processing of data (financial records, health information, government documents) that may be subject to strict data residency requirements. Cloud AI can cause data to be replicated across multiple jurisdictions, raising concerns under banking secrecy laws, the GDPR, HIPAA and other regulations. Regulators are increasingly focused on “location-based controls” or hybrid models that keep certain data within national borders or customer-controlled environments.

Legal advisers must help clients navigate this complex landscape, ensuring that cloud AI deployments comply with all relevant data protection and localisation laws. This may involve negotiating contractual commitments from cloud providers regarding data residency, as well as implementing technical controls to restrict data flows.

Security and privacy risks

Centralised cloud platforms are high-value targets for cybercriminals. Even with strong encryption and access controls, storing training or inference data in third-party environments introduces risks of breaches, misuse or unauthorised access. Shared multi-tenant architectures may also pose isolation risks, where vulnerabilities in one tenant’s environment could potentially be exploited to access another’s data.

Legal teams should ensure that cloud AI contracts include robust security obligations, incident notification requirements, and clear allocation of liability in case of a breach. They should also advise clients on conducting regular security assessments and audits of cloud providers.

Resilience and concentration risk

The global AI ecosystem is increasingly reliant on a limited number of hyperscale cloud providers. This creates systemic risk: if one provider suffers a prolonged outage or security incident, millions of AI applications could be disrupted. In regulated industries such as finance, healthcare and critical infrastructure, this concentration risk is a supervisory priority for regulators.

For example, the European Union’s Digital Operational Resilience Act (DORA) and the European Banking Authority (EBA) Outsourcing Guidelines require financial entities to assess and mitigate concentration risk in their use of third-party information and communication technology (ICT) providers. Legal advisers must help clients develop multi-cloud or hybrid strategies, and ensure that contracts include provisions for business continuity, disaster recovery, and exit in the event of provider failure.

Cost over time

While the cloud avoids large up-front capital spending, training large AI models and inference at scale can still run into millions of dollars in compute costs. Organisations must carefully manage cloud usage to avoid unexpected cost overruns.

From a legal perspective, this underscores the importance of clear contractual terms on pricing, billing transparency, and the ability to audit usage. Lawyers should also consider the implications of charges that may apply when moving data or workloads out of the cloud.

Transparency and explainability challenges

For many cloud AI services, customers do not always know how models are trained, what data is used, or how decisions are made. This lack of transparency can create compliance and liability gaps under various legal regimes.

Legal teams should work with clients to assess the transparency and explainability of their cloud AI solutions, and to ensure that contractual arrangements provide for access to necessary information, documentation and audit rights.

Regulatory pushback on outsourcing

Financial regulators, including the Office of the Comptroller of the Currency (OCC), the Federal Reserve, the Federal Deposit Insurance Corporation (FDIC), the EBA and the EU under DORA, have issued detailed guidance on outsourcing to cloud providers. The guidance requires firms to demonstrate control over outsourced services, including audit rights, exit strategies, subcontractor transparency and resilience testing. As AI adoption deepens, these requirements are extending to cloud AI, increasing the compliance burden on regulated firms.

Lawyers must ensure that cloud AI contracts are aligned with regulatory expectations, and that organisations have the necessary governance frameworks in place to manage third-party risk.

Practical strategies for managing cloud-based AI risks

Given the complexity of the legal and regulatory landscape, organisations are increasingly evolving strategies to balance the benefits of cloud-based AI with the need for control and resilience.

Hybrid AI models

Some organisations are moving towards hybrid AI architectures, using the cloud for burst capacity and innovation, while retaining sensitive workloads and data on-premises. This approach can help address data sovereignty, security and cost concerns, while still leveraging the scalability and flexibility of the cloud.

Multi-cloud strategies

To mitigate concentration and vendor lock-in risks, organisations are deploying AI solutions across multiple cloud providers. This not only enhances resilience but also provides leverage in commercial negotiations and greater flexibility to adapt to changing regulatory requirements.

Governance and oversight; employee upskilling

Effective governance is essential for managing cloud AI risks. Organisations should establish cross-functional teams, including legal, compliance, IT and business stakeholders, to oversee cloud AI deployments, monitor regulatory developments, and ensure ongoing compliance. Relatedly, organisations should invest resources in meaningfully upskilling their employees so that they can effectively keep pace with and drive innovation.

Contractual safeguards

Legal teams should negotiate robust contractual protections in cloud AI agreements, including:

• clear data residency and sovereignty commitments;
• provisions for data and model portability;
• detailed security and incident response obligations;
• audit and inspection rights;
• transparent pricing and billing terms; and
• well-defined exit and transition arrangements.

Conclusion

The cloud can help accelerate AI opportunities and innovation, combining scale, advanced hardware, cost efficiency, global collaboration and compliance support. Yet, reliance on cloud infrastructure introduces a complex web of operational, legal and strategic risks. Staying ahead of the curve will require not only technical and legal expertise, but also a proactive, collaborative approach to risk management. By embracing hybrid and multi-cloud strategies, investing in governance and oversight, and negotiating robust contractual protections, organisations can position themselves to thrive in the age of AI.