It’s well reported that AI poses both challenges and solutions for the daily functioning of data centres. Thanks to AI’s rapid development and the manner in which it functions, the technology produces a growing volume of data that data centres must handle. This is furthered by modern cybersecurity protocols, which require the replication of data to ensure resilience against potential breaches and corruption.
Here, Howes explains the impact of AI on data centre facilities and how its growing adoption presents challenges and opportunities.
How AI and its need for secure data is increasing cloud storage demand
AI has become a widely used tool in digital applications and processes for bolstering operational efficiency, with around one in six UK organisations embracing at least one AI technology according to government research. When integrated effectively, it can help with a huge number of processes, including streamlining workflows and automating mundane, repetitive processes,which frees up employees to do more productive tasks.
Similarly, the data analysis capabilities of generative AI can be a vital tool for forecasting and real-time monitoring. For instance, it can use volumes of existing data from both internal and external sources to predict things like product demand, and suggest operational adjustments like real-time pricing optimisation, or for when problems occur like supply chain disruptions.
AI tools are trained on vast quantities of data which must be stored and processed in data centres. With the AI market predicted to see an annual growth rate of 37.3% from 2023 to 2030, data volume is only going to increase as language learning models need more data to keep learning and improving. The AI revolution, therefore, will only increase the demand for cloud object storage.
As AI becomes an increasingly common and powerful operational tool, its adoption will become essential to staying competitive in a market of digitally mature, automated organisations. In turn, the volume of data, and the capacity requirements of data processing, will only continue to escalate; especially when data is appropriately backed up as a cybersecurity protocol.
Replicated data is resilient data
Because AI relies on data in order to be successful, having the right type, quality and quantity of data is essential for an AI tool to operate effectively. Proprietary AI designed for a specific company’s needs will rely on a pool of specific, proprietary business data.
As ransomware cases continue to escalate in quantity and sophistication, businesses will need to store secure backups of this data to keep it safe. If data is not backed up or stored securely, companies risk losing the value of their AI alongside the loss of the data itself. To keep data as safe as reasonably possible, companies must adopt the 3-2-1-1-0 back-up strategy to optimise the security of their back-up data, whereby they store three copies, in two or more locations, with one copy offsite and one copy stored immutably.
Immutable cloud storage means that copies are locked and cannot be compromised, deleted, or destroyed. This immutable copy is often “air gapped”, meaning it stays disconnected from the corporate network and undetectable to any who may hack this network, therefore maximising the backup’s protection.
Accordingly, the mass of data in data centres will further grow to accommodate storage space for the backup data, creating mounting pressures on both the storage capacity and efficiency of data centres. This in turn highlights an opportunity for trusted cloud storage providers as the volume of data and need for secure storage will continue to increase.
The benefit of using multiple providers
Opting for storage with one provider — for example, a hyperscaler offering a wide variety of services under one roof — risks vendor lock-in, whereby it becomes increasingly difficult to leave. This is often because migrating apps or data back can cost a fortune in egress fees and for some providers, especially if they are dependent on a cloud vendor’s apps, leaving to a different vendor becomes a complex and costly IT challenge.
Once locked in to one provider, a company’s data is at higher risk simply because, in the unlikely event of a security breach, the company risks damaging or losing the entirety of their data.
Instead, adopting a multicloud approach — whereby you opt to store data under both hyperscalers and specialty providers — bolsters the security of your data and means you are less likely to be locked in. To ensure data is adequately protected, immutable backups must be stored across multiple providers, as even in the unfortunate event of copies stored in the first cloud becoming at risk, the backups in a second vendor’s cloud will remain safe and accessible.
Companies can opt for a mix of best-of-breed speciality providers for keeping their secure backups, less commonly drawn upon data, and data of which access is more regularly required for AI training. With this multicloud approach, companies with particular security concerns can choose to invest in providers who specialise in security for their backups. Simultaneously, they can also select a vendor who provides easy data access, such as fast, fee-free ingress or egress, to easily utilise the primary copies.
Securely storing the data which an organisation depends upon to operate their AI tools ensures that these tools are able to operate effectively, boosting the efficiency of the company using them and optimising its output. As AI continues to develop, the influx of the data used to train it and subsequent influx in backup data produced to keep this data safe, provides a huge opportunity for cloud providers who have the means to store this data safely.
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