Cybersecurity: do AI and Machine Learning make a difference?
We’ve recently marked the three-year anniversary of “WannaCry”, a powerful ransomware cyberattack which infected over 200,000 computers in 150 countries over the course of just a few days. It worked by first infecting a Windows computer, then encrypting files on the PC's hard drive making them impossible for users to access and demanding a ransom payment in bitcoin in order to decrypt them. WannaCry affected everyone from individuals to large organisations like the NHS, Spanish telecom giant Telefonica and FedEx with
Although few are as successful or as devastating as “WannaCry”, there are still a huge number of cyberattacks generated by criminals each year. In 2019 alone, there were. That's simply too much volume for humans to handle.
Fortunately, technologies such as artificial intelligence (AI) and machine learning (ML) are picking up some of the slack.
Machine learning is a subset of artificial intelligence and uses algorithms born of previous datasets and statistical analysis to make assumptions about patterns of behaviour. The computer can then adjust its actions and perform functions for which it hasn’t been explicitly programmed.
With its ability to sort through millions of files and identify potentially hazardous ones, machine learning is a godsend for cybersecurity. It’s essential for uncovering threats and automatically squashing them before they can wreak havoc.
The rise of AI/ML in cybersecurity
In 2017, around the same time as the WannaCry attack, we were surveying IT decision makers across the United States and Japan on their use of AI and ML in cybersecurity, discovering that approximately 74% of businesses in both regions were already using some form of AI or ML to protect their organisations from cyber threats.
And over the last several years, its use has sustained consistent growth among businesses. When we checked in again with both regions at the end of 2018, 73% of respondents we surveyed reported they planned to use even more AI/ML tools in the following year.
Fast forward to our published this year, which surveyed 800 IT professionals with cybersecurity decision making power across the US, UK, Japan, and Australia/New Zealand regions, and we’ve discovered that 96% of respondents now use AI/ML tools in their cybersecurity programs.
However, there were some findings that left us surprised.
A lack of understanding
Despite the increase in adoption rates for these technologies, our survey found that more than half of IT decision makers admitted they do not fully understand the benefits of these tools. Even more jarring was that nearly three quarters (74%) of IT decision makers worldwide really don’t care whether they’re using AI or ML, as long as the tools they use are effective in preventing attacks.
This highlights the continued confusion and lack of knowledge regarding the use cases and capabilities of AI and machine learning-based cybersecurity tools, as well as a general distrust in their capabilities, based on how such tools are advertised by vendors.
Scepticism across geographies
Despite a small regional variance, the overall results of our survey also indicated a relatively consistent level of uncertainty across all geographies with respect to how much benefit AI/ML brings.
This really highlights that continued education and increased awareness of the benefits that the technologies bring across the industry is crucial to ensuring businesses around the world become more resilient against cyberattacks and other IT challenges.
Preparing for the future
Despite the confusion around AI and ML, most respondents planned to continue increasing spending on these technologies throughout 2020.
For these organisations, it’s crucial that they improve their understanding in order to realise maximum value.
By vetting and partnering with cybersecurity vendors who have long-standing experience using and developing AI/ML, and who can provide expert guidance, we expect businesses will be more likely to achieve the highest levels of cyber resilience, whilst efficiently maximising the capabilities of the human analysts on their teams.
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