be learnt overnight . Consequently , the perceived benefits in time and cost may take longer to achieve , which leads to frustration when the advantages and the ‘ future vision ’ are sold to those charged with leading the organisation , who are naturally looking for fast results ,” he continued .
Additionally , as professionals need to make other considerations when implementing this complex technology as Mazza explained : “ A major factor to consider is that given AI systems become more intelligent through the use of data sets businesses must collate lots of specific malware codes , non-malicious codes , and anomalies throughout implementation , it takes a lot of time and needs a significant amount of investment .”
“ Many organisations struggle to fund this . Without this ability to bring together data , AI tools can deliver incorrect results and / or false positives and getting inaccurate data from unreliable sources can even backfire .” Adding to this drawback , cyber security management defences are often fragmented across multiple different vendors . Tyley explained that with this , only some of them talk to each other : “ This makes it even more difficult for those tasked with protecting the organisation in preventing the snowballing scale of sophisticated cybercrime . In this landscape , protective systems need to be able to monitor and automate response at scale .”
He concluded that ML capabilities are the key to overcoming challenges with automated technology and cyber risk : “ ML can form a substantial part of the solution – we have seen in recent years multiple technologies that are able to capture patterns of behaviour , then , through the use of algorithms , run scenarios with the data to detect unusual patterns of activity .”
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