Cloud Computing

A balance between innovation and compliance

So far, more than 100 countries around the world have enacted data protection and data sovereignty laws, putting data sovereignty at the top of the top issues for 50% of European CXOs when selecting cloud providers . This is almost certainly because the impact on data processing complexity and management is rated as ‘moderate’ to ‘significant’ for a majority (84%) of organizations due to the number of EU regulations they face. must comply.1

Another area where data management has become part of every conversation comes in the form of generative AI. Look around your organization and you’re bound to find extended language models (LLMs) like GPT-4 used in almost every corner in one way or another, even if only informally out of curiosity or exploratory way to see what is possible. As the Times reports, its prevalence has forced some organizations to ban internal use of ChatGPT.2 Conversely, others, such as Accenture, have called on staff to use technology “responsibly”.

Those who adopt generative AI do so with productivity in mind. It is estimated that 40% of working hours across all sectors can be impacted by large language models (LLM), in which language tasks represent 62% of employees’ total working time, and 65% of that time can be transformed in more productive activity thanks to the increase. and automation.3

As the worlds of sovereign cloud and generative AI collide, we must ask whether they are able to compete, complement, or operate with each other as “enemies” – pretending to be friends but, in a way, also enemies. Since generative AI requires massive computing power and lightning-fast handling of large data sets, it makes sense that the public cloud is an ideal platform for many generative AI applications. The access, speed and scalability provided by the public cloud are only part of the equation, as security, privacy and sovereignty are vital requirements and must remain at the heart of the conversation. This translates into the message that the target cloud destination for generative AI is not in the public cloudbut preferably in a private cloud.

Finding the link between cloud and AI concerns, former British Prime Minister Tony Blair was reported by the Telegraph as suggesting that Britain should build a “sovereign” artificial intelligence (AI) robot to resolve problems that arise in public services, such as those found. in the NHS.4

This can be a complex decision. The relationship between sovereign cloud and generative AI depends on several important factors, as striking a balance between data sovereignty, privacy, compliance and the benefits of generative AI requires careful consideration and could involve a trade-off between unusual solutions. Box on the capabilities and limitations of public cloud generative AI regarding the use of sovereign data.

Data Residency and Compliance

Generative AI applications may need to comply with a range of regulations, such as the data protection rules outlined in the General Data Protection Regulation (GDPR), which provides a comprehensive framework for the collection, processing and Responsible storage of personal data by organizations.5 However, this is just the tip of the iceberg, as rights-based approaches to personal data continue to expand across regions, nations, states and even sectors. In the United States, a multitude of new data privacy laws Taking effect in 2023 in various states could transform how personal data is processed.1 Given the widespread use of AI-based personal information analysis and even biometrics, organizations like the Federal Trade Commission are wary of how bias in AI can economically and legally impact private citizens. Courts in every region are already considering exactly how businesses, government agencies and industries can use personal data. On the other hand, sovereign clouds may need to adapt to a different set of regulatory frameworks and include data classifications, such as the rules outlined in the Schrems II ruling, in which data protection officials data needs to understand and evaluate what data is stored in the cloud. cloud and if any of this data is transferred outside the EU.6 Ensuring compliance for generative AI in the cloud will introduce complexities, competing requirements, and additional compliance costs for AI.

However, if a well-designed sovereign cloud strategy is adopted, it could help overcome these challenges and complexities by:

  • Ensure compliance with local regulations where the sovereign cloud resides
  • Reduce legal uncertainties surrounding applications and datasets
  • Eliminate conflicts arising from different laws of competing jurisdictions

Data sovereignty and privacy

Generative AI applications likely need access to personal or proprietary data for training (although synthetic data could be used, it would be less effective), introducing risks associated with data sovereignty and privacy . Although sovereign clouds provide greater control over data to reduce the risk of unauthorized access, these controls would help limit cross-border transfers and monetization of sovereign data used by these generative AI applications, potentially reducing or, in extreme cases, eliminating the benefits of applications.

While these and other privacy-related conflicts may develop when generative AI is connected to or within the sovereign cloud, there is an opportunity for sovereign cloud providers to create (or license under license) AI-related services and tools, facilitating the secure and compliant use of generative AI applications within their infrastructure.

A viable path forward

While the considerations highlighted in this blog should be taken into account when using generative AI, sovereign cloud provides a proven model that allows organizations to overcome challenges related to:

  • Data protection
  • Compliance requirements
  • Security Considerations

A robust sovereign cloud strategy can support the responsible and effective use of generative AI technologies by ensuring data residency, security, and compliance with local governments. Such a strategy should include AI compatibility, factors such as operating costs of AI, resources and skills required to develop AI solutions, and the main concern, the data used to drive the AI training and the resulting data. In short, making AI compliant requires “private AI”: an architectural approach that unlocks the business benefits of AI while meeting an organization’s practical privacy and compliance needs. VMware Cloud Service Providers have already been able to deliver NVIDIA vGPU as a Service for several years, combining Cloud Director for multi-tenant or standalone Cloud Foundation sovereign cloud with the portfolio of AI software and hardware solutions supported by NVIDIA AI Enterprise; capacity already exists to provide AI services; However, creating your solution requires an investment.

To power a new wave of AI-driven applications, make private AI a reality for businesses and a potential solution for sovereign cloud providers, VMware announced at Explore Las Vegas:

  • VMware Private AI Foundation, in partnership with NVIDIA, offers integrated AI tools for businesses, particularly relevant in sovereign regulated industries. This solution combines VMware’s Private AI architecture, leveraging VMware Cloud Foundation, often used in Sovereign Cloud for isolated private cloud, with NVIDIA AI Enterprise software and accelerated computing. This turnkey solution will provide customers with the infrastructure and software needed to customize models and run generative AI applications such as chatbots, assistants, search and summarization. Support for VMware Private AI Foundation with NVIDIA will be provided by Dell Technologies, Hewlett Packard Enterprise (HPE), and Lenovo.
  • VMware Private AI Reference Architecture for Open Source integrates cutting-edge open source technologies to provide an open framework for developing and deploying open source models on VMware Cloud Foundation. Utilize collaborations with key players in the AI ​​industry, including Anyscale, where VMware brings the popular open source unified compute framework Ray to VMware Cloud environments, simplifying the scaling of AI and Python workloads to within the existing IT infrastructure. Additionally, a partnership with Domino Data Lab and NVIDIA provides a unified analytics and data science platform suitable for financial industry AI/ML deployments.
  • VMware presents the VMware AI Ready Programdesigned to connect software companies focused on ML, LLM operations, data engineering, development tools for AI and embedded AI applications with validation and certification resources on architecture reference VMware Private AI, which is scheduled to launch by the end of 2023.

Sovereign Cloud is about the data, control and innovation that sovereign and regulated industries need. Staying at the forefront of innovation and competition is essential for businesses in a rapidly evolving digital landscape. VMware Sovereign Clouds appear to be the ideal choice for this journey. Their flexibility, scalability and agility enable organizations to quickly adapt to changing market dynamics, enabling them to innovate with confidence. VMware’s robust ecosystem of partners and cutting-edge technologies ensures you have the tools and resources needed to advance innovation. With VMware Sovereign Cloud, businesses can harness the power of the cloud while maintaining control of their data and infrastructure, creating an environment where innovation thrives, competition is fierce, and success is limitless. Make the smart choice today and propel your business into a future of endless possibilities with VMware Cloud.


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