Artificial intelligence (AI) is hardly a new idea. In fact, the first reference to what we now understand as AI dates back to 1863, when Samuel Butler wrote Darwin among the Machines. Butler’s article predicted the rise of “mechanical consciousness” and, like the writers who came after him, the picture he painted was somewhat dystopic.
We now have the privilege of witnessing the world that Butler and so many other writers foresaw. Unlike the inhabitants of their fictional futures, we’re not unduly worried about self-aware robots seizing control. Instead, our concerns are much more prosaic – though the consequences of out-of-control AI are scarcely less significant than they envisaged.
Artificial intelligence throws up a host of thorny problems, ranging from the ethical dilemma inherent in driverless cars to the question of AI chatbots failing to understand certain accents.
It's easy to catastrophise about the baleful influence of AI, but the truth is that none of the challenges it presents are insoluble. What’s more, AI has the potential to help us solve one of the biggest challenges in modern life: managing the vast volumes of data we generate every day. Few of our ancestors imagined that the 21st century would be driven to such a huge extent by information, and how each internet-connected human can create a huge and ever-growing trail of personal data.
Human error has often been considered one of the biggest threats to data security. Replacing complex manual processes with intelligent data management strategies, coupled with AI and its cousin, machine learning (ML), can not only enhance information management but even elevate it to its true potential. For example, by improving the way that organisations monitor systems health, detect potential issues and create proactive remediation before problems can occur.
We stand on the verge of a truly exciting future, where information is able to manage itself – but to achieve this, organisations need to be able to ensure that their data is fit to work with AI and ML technologies.
These technological advances couldn’t have come at a more opportune moment: over the course of the last three years more than two billion individuals were affected by data breaches—each breach targeting organisations, their customers and ultimately, customers’ private data. In addition, Veritas’ Data Genomics Index, which provides a benchmark analysis of data stored within a typical company environment, revealed that inside every petabyte of enterprise data (which is larger than 15,000 smartphones in average storage capacity), there are over three billion individual files. Each file contains the potential to house some vital data that is critically valuable to the organisation.
Getting a handle on the data
If anything, these phenomenal figures show that humans are losing the battle to keep data secure through traditional means. With AI and ML transforming our world in so many other ways, we now have the opportunity to harness these technologies to enhance and safeguard our information.
But to be successful, organisations need to ensure that their data is properly maintained and managed – a task that most businesses have barely begun. Our research found that less than a third (29 per cent) of organisations have adopted a strategic, fully integrated approach to data management deployment. Without a clean dataset, the limitless benefits of AI and ML cannot be realised.
So how does a business begin to get a handle on its data in preparation for tomorrow’s AI and ML technologies? The best method is a three-stage data management process, focusing on protection, classification and action.
First, organisations must ensure they have the ability to respond to threats and losses and recover quickly. Data must be available and quickly accessible to those who require it. Secondly, businesses must also be able to classify data, ensuring they can understand and account for all the information they own and automatically categorise it.
Once all data is effectively protected and classified, data can essentially manage itself, enabling processes, policies and artificial intelligence to create meaningful insights from data sets that can open doors for endless opportunities such as new revenue streams, R&D, and better customer experiences. In addition, with GDPR, organisations can put in place policies and processes which help automatically delete and modify data that doesn’t comply with the regulation, which limits the room for human error.
It's safe to say that even the most prescient writers of yesteryear would be amazed at the potential of today’s technologies. If we are to achieve their full power, however, we need to have the firmest possible grip over today’s most precious digital commodity: data.
By applying the principles outlined above, we open up the opportunity for AI and ML projects to flourish, enabling data to manage data without the fear of breaches. The result will be that technology no longer becomes an ever-present threat to our data security, and instead becomes our most powerful ally in wringing the maximum possible value from the information trapped in our vast, labyrinthine repositories.
There is no reason to fear the rise of artificial intelligence – but it requires us to act now to build the future that we want to inhabit.
Jasmit Sagoo, Senior Director, Northern Europe, Veritas Technologies
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