AI transformation has become the strategic priority de jour for organisations looking to innovate and stay relevant in the digital age. The ability to rapidly analyse vast amounts of data and provide valuable insights makes it a crucial tool for decision-makers.

Hundreds of billions of dollars are being invested globally in AI. Everyone in tech from the webscalers to the high-street; plus governments, financial services companies, healthcare providers, manufacturers, aviation, automotive, agriculture – the list goes on. Not surprisingly, industry analyst Gartner identified democratised generative AI, AI trust, risk and security management, and AI-augmented development as the top three strategic technology trends for 2024.

Consumers have been fast to adopt the technology. ChatGPT reached 1 million users five days after its launch in late 2022. In some quarters AI has been characterised as a threat to jobs, in others a threat to humanity. Several countries block access to ChatGPT, including obvious candidates like North Korea and China, but also less obvious candidates like Italy, which introduced a temporary ban last year due to privacy concerns and continues to be wary. No matter, at the last count more than 180 million people have created accounts and 100 million people access it weekly.

AI-driven solutions, algorithms, and technologies have the potential to help businesses improve efficiencies, boost customer satisfaction and deliver competitive advantage. But AI transformation is not without its challenges. The power of AI lies in data, yet according to McKinsey, 72% of organisations say managing data is a key challenge preventing them from scaling AI.

Where that management takes place will be crucial to solving the challenge. That ‘place’ will undoubtedly be the cloud where, as of 2022, 60% of the world’s corporate data was being stored. By 2027, Gartner forecasts over 70% of enterprises will use industry cloud platforms to drive business initiatives, up from less than 15% in 2023.

In the very near future the vast majority of corporate data will be stored and accessed on public and private cloud platforms, or more likely will be spread globally across a complex hybrid landscape. That landscape of cloud platforms will be both virtual and physical in the sense that they’ll operate out of a network of interconnected data centres dotted around the world.

The physical location of data centres brings with it additional question marks over data sovereignty and security. That’s a huge part of the reason NexGen Cloud is investing $1 billion to build the European AI Supercloud. Data sovereignty is the concept that data is subject to the laws of the country in which it is located. Anyone familiar with data privacy and GDPR will know about data sovereignty since data collected on individuals in the EU must be either stored in the EU, so it is subject to European privacy laws, or within a jurisdiction that has similar levels of protection. Collecting, storing and acting on data is one thing, keeping it safe and/or sharing it (especially cross border) is a whole new regulatory ball game.

And the regulations on data privacy are far from toothless. Last year, the Irish regulator fined social media giants TikTok and Meta €345m and €1.2bn respectively. Outside Europe meanwhile ride-hailing firm Didi Global was fined $1.19 billion by the Cyberspace Administration of China. High profile cases like these are the tip of the iceberg.
In addition to direct financial repercussions associated with breaching regulations come indirect problems with what could arguably be much longer-term impacts such as a loss of trust among customers and partners.

As noted previously, there is a certain level of distrust regarding AI – the figures vary, one survey in the US found 79% of respondents reported trusting businesses “not much” or “not at all” to adopt AI responsibly. While the Office for National Statistics in the UK reported 36% adults said they did not think AI could have a positive impact on their lives. It would not take too many data breaches to push those percentages up higher.
The critical interplay between ensuring data sovereignty and robust cloud security measures is at the heart of ensuring organisations capitalise on the advantages promised by adopting and transforming business processes with AI.

Organisations face a wide variety of security threats associated with cloud sovereignty including but not limited to data jurisdiction and legal compliance, data access control issues, cross-border data transfers, vendor compliance and transparency, supply chain security risks, data encryption and decryption challenges, inadequate incident response plans, national security concerns and broader political and geopolitical risks.
The relationship between data sovereignty and cloud security is entwined. Organisations must navigate data governance to ensure compliance with regional regulations to safely and ethically harness the power of AI. They must also prioritise robust cloud security and data sovereignty when choosing partners in their AI journey.

The promises of AI appear limitless, but mitigating security challenges necessitates a holistic strategy that integrates technical safeguards, adherence to legal requirements, and effective risk management practices.

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