TECH TRENDS
Balancing usability and security in the age of AI and regulation
Keeping data safe is a crucial aspect of the modern business . And it has become more difficult to navigate in recent times due to digitisation and the large amount of data collected , stored and used . Dave Russell , Senior Vice President , Head of Strategy at Veeam Software , explains how companies should handle their data .
Keeping data both safe and easily accessible has been a challenge for organisations since , well , since the first paper file was stored away . Admittedly , over the last couple of decades , this has become much trickier to navigate – digitisation means the sheer amount of data collected , stored and used has grown exponentially . And now , we ’ re seeing another data growth spurt due to widespread AI adoption .
Meanwhile , governments worldwide are doing their best to keep up , introducing growing levels of data regulations seemingly every year . This puts organisations under increased pressure to ensure data resilience as they get to grips with this new age of AI . They ’ ve been left to walk a tightrope between ensuring that data is usable for business use while also keeping it secure and resilient , in line with evolving regulations .
Dude , where ’ s my data ?
With the widely acclaimed promise of AI , the demands on enterprise data have never been greater – requiring it to be accurate , accessible and usable at all times . While the initial excitement around Generative AI has quietened , organisations are now adopting the technology in earnest to unlock increased business value from all that existing data . According to the latest McKinsey Global Survey on AI , 65 % of respondents worldwide reported that their organisations are regularly using AI . But what does this mean for data resilience ?
Well , it ’ s no secret that AI relies on data . Some would say the more data the better , but the wiser approach is the more accurate and relevant data , the better . While some AI applications might only need to be trained once , most require live access to a data pool to analyse and react to changes in real-time . Any inaccuracies or inconsistencies in data across an organisation can quickly render AI ’ s output useless . As the adage goes : garbage in , garbage out . Of course , it ’ s important to be careful about what data you feed the beast , namely any sensitive , mission-critical or customer data . There ’ s very much still a balance to be figured out as more and more organisations embrace AI .
What should help organisations strike this balance is the wave of regulations demanding greater data resilience and responsibility both in AI and more broadly . These regulations , including NIS2 and the EU AI Act , have all placed increased responsibility on organisations to ensure data security and rightly so . This new wave of data regulation focuses largely on extending the line of custody that organisations have on their data , requiring them to consider how it will be secured when plugged into AI and other new technologies . When data was originally collected and stored , organisations likely didn ’ t have AI on their radar , let alone consider how their data might be used in such technologies . While these new considerations fall primarily under the responsibility of chief information governance teams , achieving compliance with AI-related
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